{"id":36812,"date":"2026-05-20T11:04:41","date_gmt":"2026-05-20T11:04:41","guid":{"rendered":"https:\/\/aisuperior.com\/?p=36812"},"modified":"2026-05-20T11:04:41","modified_gmt":"2026-05-20T11:04:41","slug":"machine-learning-in-aerospace-industry","status":"publish","type":"post","link":"https:\/\/aisuperior.com\/fr\/machine-learning-in-aerospace-industry\/","title":{"rendered":"Apprentissage automatique dans l&#039;a\u00e9rospatiale : guide de l&#039;industrie 2026"},"content":{"rendered":"<p><b>R\u00e9sum\u00e9 rapide\u00a0: <\/b><span style=\"font-weight: 400;\">L&#039;apprentissage automatique r\u00e9volutionne l&#039;a\u00e9rospatiale gr\u00e2ce \u00e0 la navigation autonome des engins spatiaux, la maintenance pr\u00e9dictive et la conception optimis\u00e9e des a\u00e9ronefs. Le rover Perseverance de la NASA (88%) d\u00e9montre sa capacit\u00e9 de conduite autonome gr\u00e2ce \u00e0 l&#039;analyse du terrain par apprentissage automatique, tandis que des organismes de r\u00e9glementation comme l&#039;AESA et la FAA \u00e9tablissent des cadres de confiance pour l&#039;IA dans l&#039;aviation. De l&#039;efficacit\u00e9 de la production \u00e0 l&#039;am\u00e9lioration de la s\u00e9curit\u00e9, les applications d&#039;apprentissage automatique couvrent l&#039;ensemble du cycle de vie a\u00e9rospatial, permettant une prise de d\u00e9cision fond\u00e9e sur les donn\u00e9es et une excellence op\u00e9rationnelle.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">L&#039;industrie a\u00e9rospatiale a toujours repouss\u00e9 les limites technologiques. Aujourd&#039;hui, l&#039;apprentissage automatique porte cette innovation \u00e0 des niveaux sans pr\u00e9c\u00e9dent.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Des engins spatiaux prenant des d\u00e9cisions autonomes \u00e0 des millions de kilom\u00e8tres de la Terre aux syst\u00e8mes d&#039;a\u00e9ronefs pr\u00e9disant les besoins de maintenance avant les pannes, l&#039;apprentissage automatique ne se contente pas d&#039;am\u00e9liorer les op\u00e9rations a\u00e9rospatiales. Il transforme en profondeur la mani\u00e8re dont l&#039;industrie con\u00e7oit, fabrique et exploite ces technologies.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Le point essentiel est le suivant\u00a0: l\u2019apprentissage automatique dans l\u2019a\u00e9rospatiale ne consiste pas \u00e0 appliquer des algorithmes \u00e0 tout-va en esp\u00e9rant un miracle. Il s\u2019agit de r\u00e9soudre des probl\u00e8mes sp\u00e9cifiques, complexes et n\u00e9cessitant un traitement important des donn\u00e9es, qui affectent le secteur depuis des d\u00e9cennies.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Comment l&#039;apprentissage automatique alimente les syst\u00e8mes a\u00e9rospatiaux autonomes<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">L&#039;autonomie des engins spatiaux repr\u00e9sente l&#039;une des r\u00e9alisations les plus impressionnantes de l&#039;apprentissage automatique dans le domaine a\u00e9rospatial. Lorsque les d\u00e9lais de communication atteignent plusieurs minutes, voire plusieurs heures, la prise de d\u00e9cision autonome devient essentielle et non plus une option.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Selon la NASA, l&#039;intelligence artificielle permet aux engins spatiaux de prendre des d\u00e9cisions de mani\u00e8re autonome et de continuer \u00e0 fonctionner m\u00eame hors de contact avec la Terre. Les r\u00e9sultats sont \u00e9loquents\u00a0: 881\u00a0% des d\u00e9placements du rover Perseverance ont \u00e9t\u00e9 enti\u00e8rement autonomes.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Le processus repose sur une analyse d&#039;images continue. Le rover acquiert des images du terrain gr\u00e2ce \u00e0 ses cam\u00e9ras, les analyse avec un ordinateur embarqu\u00e9 pour identifier les dangers et les chemins s\u00fbrs, puis effectue des mouvements sans attendre de commandes depuis la Terre.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Mais l&#039;autonomie ne se limite pas aux rovers plan\u00e9taires. Les constellations de satellites utilisent l&#039;apprentissage automatique pour \u00e9viter les collisions, ajuster leur orbite et optimiser leur charge utile, le tout de mani\u00e8re ind\u00e9pendante, tandis que les \u00e9quipes bas\u00e9es sur Terre se concentrent sur la supervision strat\u00e9gique plut\u00f4t que sur le contr\u00f4le tactique.<\/span><\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone size-full wp-image-35586\" src=\"https:\/\/aisuperior.com\/wp-content\/uploads\/2026\/04\/Superior.webp\" alt=\"\" width=\"434\" height=\"116\" srcset=\"https:\/\/aisuperior.com\/wp-content\/uploads\/2026\/04\/Superior.webp 434w, https:\/\/aisuperior.com\/wp-content\/uploads\/2026\/04\/Superior-300x80.webp 300w, https:\/\/aisuperior.com\/wp-content\/uploads\/2026\/04\/Superior-18x5.webp 18w\" sizes=\"(max-width: 434px) 100vw, 434px\" \/><\/p>\n<h2><span style=\"font-weight: 400;\">Transformer les donn\u00e9es a\u00e9rospatiales en syst\u00e8mes d&#039;apprentissage automatique op\u00e9rationnels<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Les organisations a\u00e9rospatiales utilisent l&#039;apprentissage automatique pour am\u00e9liorer la s\u00e9curit\u00e9 et r\u00e9duire les risques. <\/span><a href=\"https:\/\/aisuperior.com\/fr\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">IA sup\u00e9rieure<\/span><\/a><span style=\"font-weight: 400;\"> propose des solutions d&#039;IA et d&#039;apprentissage automatique personnalis\u00e9es pour les secteurs complexes.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Concevoir une solution d&#039;apprentissage automatique pour les projets a\u00e9rospatiaux<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">AI Superior soutient des projets d&#039;apprentissage automatique dans le secteur a\u00e9rospatial, notamment\u00a0:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Maintenance pr\u00e9dictive et d\u00e9tection d&#039;anomalies<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Vision par ordinateur pour l&#039;inspection et le contr\u00f4le qualit\u00e9<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Traitement automatique du langage naturel pour la documentation technique et l&#039;extraction de donn\u00e9es<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">\ud83d\udc49<\/span><a href=\"https:\/\/aisuperior.com\/fr\/contact\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">Contactez l&#039;IA sup\u00e9rieure<\/span><\/a><span style=\"font-weight: 400;\"> pour discuter de votre projet d&#039;apprentissage automatique dans le domaine a\u00e9rospatial.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Maintenance pr\u00e9dictive\u00a0: pr\u00e9venir les pannes avant qu\u2019elles ne surviennent<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">La maintenance des a\u00e9ronefs a traditionnellement suivi des calendriers rigides\u00a0: inspection ou remplacement des composants apr\u00e8s X heures de vol, ind\u00e9pendamment de leur \u00e9tat r\u00e9el. L\u2019apprentissage automatique change compl\u00e8tement la donne.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">La maintenance pr\u00e9dictive exploite les donn\u00e9es des capteurs, l&#039;historique des performances et la surveillance en temps r\u00e9el pour anticiper les pannes de composants avant qu&#039;elles ne surviennent. Les compagnies a\u00e9riennes peuvent d\u00e9sormais remplacer les pi\u00e8ces en fonction de l&#039;usure r\u00e9elle plut\u00f4t que d&#039;intervalles de temps arbitraires.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">La FAA reconna\u00eet que l&#039;intelligence artificielle permet de cr\u00e9er des syst\u00e8mes informatiques qui am\u00e9liorent l&#039;efficacit\u00e9 et la performance du contr\u00f4le des syst\u00e8mes d&#039;a\u00e9ronefs. L&#039;apprentissage automatique applique des m\u00e9thodes informatiques pour entra\u00eener des mod\u00e8les d&#039;IA \u00e0 apprendre \u00e0 partir de donn\u00e9es et \u00e0 g\u00e9n\u00e9raliser ces connaissances en algorithmes compacts.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Soyons clairs\u00a0: les avantages vont bien au-del\u00e0 de la pr\u00e9vention des pannes en vol. La maintenance pr\u00e9dictive r\u00e9duit les remplacements de pi\u00e8ces inutiles, optimise la gestion des stocks et minimise les temps d\u2019arr\u00eat impr\u00e9vus, ce qui se traduit par des \u00e9conomies substantielles et une s\u00e9curit\u00e9 accrue.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">R\u00e9volutionner la conception et la fabrication des a\u00e9ronefs<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">La conception a\u00e9rospatiale implique d&#039;innombrables it\u00e9rations, simulations et cycles d&#039;optimisation. L&#039;apprentissage automatique acc\u00e9l\u00e8re ces processus tout en explorant des espaces de conception que les ing\u00e9nieurs humains n&#039;envisageraient jamais.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Il existe une id\u00e9e fausse r\u00e9pandue concernant l&#039;apprentissage automatique\u00a0: on le per\u00e7oit comme une technologie \u2018\u00a0magique\u00a0\u2019 applicable \u00e0 tous les domaines. Or, dans le secteur a\u00e9rospatial, fortement d\u00e9pendant des donn\u00e9es, l&#039;apprentissage automatique peut tirer de nombreux avantages, notamment une am\u00e9lioration de la rapidit\u00e9 et de la pr\u00e9cision des activit\u00e9s de conception, de fabrication et de maintenance.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Les mod\u00e8les d&#039;apprentissage automatique analysent simultan\u00e9ment les performances a\u00e9rodynamiques, l&#039;int\u00e9grit\u00e9 structurelle, le rendement \u00e9nerg\u00e9tique et les contraintes de fabrication, identifiant ainsi les configurations optimales plus rapidement que les m\u00e9thodes traditionnelles. Ce qui n\u00e9cessitait auparavant des semaines de simulations de dynamique des fluides num\u00e9rique peut d\u00e9sormais \u00eatre r\u00e9alis\u00e9 en quelques heures.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Les processus de fabrication en b\u00e9n\u00e9ficient \u00e9galement. Les syst\u00e8mes de vision par ordinateur d\u00e9tectent les d\u00e9fauts des mat\u00e9riaux composites lors de la stratification, les algorithmes d&#039;apprentissage automatique optimisent les param\u00e8tres d&#039;usinage CNC pour les composants complexes et les syst\u00e8mes de contr\u00f4le qualit\u00e9 identifient les anomalies qui pourraient \u00e9chapper aux inspecteurs humains.<\/span><\/p>\n<table>\n<thead>\n<tr>\n<th><span style=\"font-weight: 400;\">Applications a\u00e9rospatiales<\/span><\/th>\n<th><span style=\"font-weight: 400;\">Approche d&#039;apprentissage automatique<\/span><\/th>\n<th><span style=\"font-weight: 400;\">Avantage principal<\/span><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><span style=\"font-weight: 400;\">Navigation autonome<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Vision par ordinateur + arbres de d\u00e9cision<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u00c9vitement des dangers en temps r\u00e9el<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Maintenance pr\u00e9dictive<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Analyse des s\u00e9ries temporelles + R\u00e9seaux de neurones<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Pr\u00e9vention des d\u00e9faillances<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Optimisation de la conception<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Algorithmes g\u00e9n\u00e9tiques + apprentissage par renforcement<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Am\u00e9lioration des performances<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Contr\u00f4le de qualit\u00e9<\/span><\/td>\n<td><span style=\"font-weight: 400;\">R\u00e9seaux neuronaux convolutifs<\/span><\/td>\n<td><span style=\"font-weight: 400;\">D\u00e9tection des d\u00e9fauts<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Optimisation de la trajectoire de vol<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Mod\u00e8les de r\u00e9gression + Clustering<\/span><\/td>\n<td><span style=\"font-weight: 400;\">consommation de carburant<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span style=\"font-weight: 400;\">Cadres r\u00e9glementaires : Instaurer la confiance dans l&#039;IA a\u00e9rospatiale<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Face \u00e0 l&#039;importance croissante des syst\u00e8mes d&#039;apprentissage automatique pour la s\u00e9curit\u00e9, les organismes de r\u00e9glementation ont rapidement mis en place des cadres de fiabilit\u00e9. L&#039;AESA a publi\u00e9 l&#039;avis de proposition de modification (NPA) 2025-07 le 10 novembre 2025 afin de fournir au secteur des orientations techniques sur la fiabilit\u00e9 de l&#039;IA, conform\u00e9ment \u00e0 la r\u00e9glementation europ\u00e9enne sur l&#039;IA.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Les objectifs sont de soutenir le d\u00e9ploiement de l&#039;IA dans les domaines sp\u00e9cifiques de l&#039;aviation identifi\u00e9s dans l&#039;article 108 de la loi europ\u00e9enne sur l&#039;IA et d&#039;\u00e9tablir un cadre r\u00e9glementaire complet de fiabilit\u00e9 de l&#039;IA qui permettra un d\u00e9ploiement potentiellement transparent de l&#039;IA dans d&#039;autres domaines de l&#039;aviation \u00e0 l&#039;avenir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">La NASA a cr\u00e9\u00e9 un nouveau groupe d&#039;int\u00e9r\u00eat scientifique et technologique sur l&#039;IA\/ML (STIG) au sein du groupe d&#039;analyse du programme sur les origines cosmiques le 6 octobre 2025. Ces initiatives font progresser des sous-domaines sp\u00e9cifiques gr\u00e2ce \u00e0 des r\u00e9unions r\u00e9guli\u00e8res et au partage des connaissances \u00e0 un moment critique pour le d\u00e9veloppement de l&#039;IA a\u00e9rospatiale.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Certes, la r\u00e9glementation peut para\u00eetre comme un obstacle bureaucratique. Pourtant, les cadres normalis\u00e9s acc\u00e9l\u00e8rent en r\u00e9alit\u00e9 l&#039;adoption du ML en offrant des voies de conformit\u00e9 claires et en renfor\u00e7ant la confiance des parties prenantes dans les syst\u00e8mes pilot\u00e9s par l&#039;IA.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Optimisation de la planification des missions et des op\u00e9rations<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Les missions spatiales n\u00e9cessitent une planification complexe prenant en compte d&#039;innombrables variables\u00a0: fen\u00eatres de lancement, m\u00e9canique orbitale, allocation des ressources, calendriers de communication et sc\u00e9narios d&#039;urgence. L&#039;apprentissage automatique excelle dans l&#039;optimisation de ces probl\u00e8mes complexes \u00e0 contraintes multiples.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">La NASA utilise l&#039;intelligence artificielle pour soutenir ses missions et ses projets de recherche, analyser les donn\u00e9es afin de r\u00e9v\u00e9ler les tendances et les sch\u00e9mas, et d\u00e9velopper des syst\u00e8mes capables de prendre en charge les engins spatiaux et les a\u00e9ronefs de mani\u00e8re autonome.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Les pr\u00e9visions m\u00e9t\u00e9orologiques pour les op\u00e9rations a\u00e9rospatiales ont connu une am\u00e9lioration spectaculaire gr\u00e2ce aux mod\u00e8les d&#039;apprentissage automatique qui traitent d&#039;immenses ensembles de donn\u00e9es atmosph\u00e9riques. Les pr\u00e9visions de lancement, le routage des vols et les ajustements du calendrier des missions s&#039;appuient d\u00e9sormais sur des renseignements m\u00e9t\u00e9orologiques plus pr\u00e9cis que jamais.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Analyse des donn\u00e9es et identification des tendances<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Le secteur a\u00e9rospatial g\u00e9n\u00e8re d&#039;\u00e9normes volumes de donn\u00e9es\u00a0: flux de t\u00e9l\u00e9m\u00e9trie, relev\u00e9s de capteurs, carnets de vol, indicateurs de production et rapports de maintenance. Les m\u00e9thodes d&#039;analyse traditionnelles ne permettent pas de traiter efficacement ces ensembles de donn\u00e9es.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">L&#039;apprentissage automatique excelle dans la d\u00e9tection de sch\u00e9mas que les humains ne remarqueraient jamais\u00a0: corr\u00e9lations subtiles entre les conditions environnementales et l&#039;usure des composants, relations inattendues entre les param\u00e8tres de vol et la consommation de carburant, ou encore indicateurs pr\u00e9coces de probl\u00e8mes syst\u00e9miques affectant les flottes d&#039;a\u00e9ronefs.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">La NASA souligne comment l&#039;intelligence artificielle contribue \u00e0 l&#039;analyse des donn\u00e9es afin de r\u00e9v\u00e9ler des tendances et des sch\u00e9mas r\u00e9currents dans les missions et les projets de recherche de l&#039;agence. Ces informations permettent une am\u00e9lioration continue des syst\u00e8mes et des op\u00e9rations a\u00e9rospatiales.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Questions fr\u00e9quemment pos\u00e9es<\/span><\/h2>\n<div class=\"schema-faq-code\">\n<div class=\"faq-question\">\n<h3 class=\"faq-q\">Quelles sont les principales applications de l&#039;apprentissage automatique dans le domaine a\u00e9rospatial\u00a0?<\/h3>\n<div>\n<p class=\"faq-a\">Les principales applications comprennent la navigation autonome des engins spatiaux, la maintenance pr\u00e9dictive des syst\u00e8mes d&#039;a\u00e9ronefs, l&#039;optimisation de la conception des a\u00e9ronefs, le contr\u00f4le de la qualit\u00e9 de la fabrication, la planification des trajectoires de vol et l&#039;optimisation des op\u00e9rations de mission. La NASA illustre ces capacit\u00e9s avec le rover Perseverance, qui a atteint une vitesse de conduite autonome de 881 TP3T gr\u00e2ce \u00e0 l&#039;analyse du terrain par apprentissage automatique.<\/p>\n<\/div>\n<\/div>\n<div class=\"faq-question\">\n<h3 class=\"faq-q\">Comment l&#039;apprentissage automatique am\u00e9liore-t-il la s\u00e9curit\u00e9 a\u00e9rospatiale\u00a0?<\/h3>\n<div>\n<p class=\"faq-a\">L&#039;apprentissage automatique am\u00e9liore la s\u00e9curit\u00e9 gr\u00e2ce \u00e0 la maintenance pr\u00e9dictive qui pr\u00e9vient les pannes avant qu&#039;elles ne surviennent, aux syst\u00e8mes de d\u00e9tection d&#039;anomalies qui identifient les probl\u00e8mes plus t\u00f4t que les m\u00e9thodes traditionnelles, \u00e0 la prise de d\u00e9cision autonome qui r\u00e9agit plus rapidement que les op\u00e9rateurs humains dans les situations critiques et \u00e0 un contr\u00f4le qualit\u00e9 am\u00e9lior\u00e9 pendant la fabrication qui d\u00e9tecte les d\u00e9fauts que les inspecteurs humains pourraient manquer.<\/p>\n<\/div>\n<\/div>\n<div class=\"faq-question\">\n<h3 class=\"faq-q\">Quels cadres r\u00e9glementaires r\u00e9gissent l&#039;IA dans le secteur a\u00e9rospatial ?<\/h3>\n<div>\n<p class=\"faq-a\">L\u2019AESA a publi\u00e9 l\u2019avis de proposition de r\u00e9glementation (NPA) 2025-07 le 10 novembre 2025, fournissant des orientations techniques sur la fiabilit\u00e9 de l\u2019IA, conform\u00e9ment \u00e0 la loi europ\u00e9enne sur l\u2019IA. La FAA d\u00e9finit les disciplines techniques relatives \u00e0 l\u2019intelligence artificielle et \u00e0 l\u2019apprentissage automatique dans l\u2019aviation. La NASA a cr\u00e9\u00e9 un groupe d\u2019int\u00e9r\u00eat scientifique et technologique sur l\u2019IA et l\u2019apprentissage automatique le 6 octobre 2025 afin de promouvoir les applications de l\u2019apprentissage automatique dans le domaine a\u00e9rospatial, dans le respect des cadres de s\u00e9curit\u00e9 \u00e9tablis.<\/p>\n<\/div>\n<\/div>\n<div class=\"faq-question\">\n<h3 class=\"faq-q\">L&#039;apprentissage automatique peut-il r\u00e9duire les co\u00fbts op\u00e9rationnels du secteur a\u00e9rospatial\u00a0?<\/h3>\n<div>\n<p class=\"faq-a\">Absolument. La maintenance pr\u00e9dictive r\u00e9duit les remplacements de pi\u00e8ces inutiles et les temps d&#039;arr\u00eat impr\u00e9vus. L&#039;optimisation de la conception diminue la consommation de carburant et les co\u00fbts de fabrication. Les syst\u00e8mes autonomes r\u00e9duisent les besoins en personnel d&#039;exploitation. L&#039;automatisation du contr\u00f4le qualit\u00e9 d\u00e9tecte les d\u00e9fauts plus t\u00f4t, lorsqu&#039;ils sont moins co\u00fbteux \u00e0 corriger. Ces avantages combin\u00e9s permettent de r\u00e9aliser des \u00e9conomies substantielles sur l&#039;ensemble des op\u00e9rations a\u00e9rospatiales.<\/p>\n<\/div>\n<\/div>\n<div class=\"faq-question\">\n<h3 class=\"faq-q\">Comment l&#039;apprentissage automatique permet-il l&#039;autonomie des engins spatiaux\u00a0?<\/h3>\n<div>\n<p class=\"faq-a\">L&#039;apprentissage automatique permet aux engins spatiaux d&#039;analyser les donn\u00e9es des capteurs, d&#039;identifier les dangers, de prendre des d\u00e9cisions de navigation et d&#039;ex\u00e9cuter des man\u0153uvres sans attendre de commandes terrestres. Cette capacit\u00e9 devient essentielle lorsque les d\u00e9lais de communication atteignent plusieurs minutes, voire plusieurs heures. L&#039;engin spatial traite les images des cam\u00e9ras embarqu\u00e9es, reconna\u00eet le relief, planifie des trajectoires s\u00fbres et fonctionne en continu, m\u00eame hors de port\u00e9e du centre de contr\u00f4le.<\/p>\n<\/div>\n<\/div>\n<div class=\"faq-question\">\n<h3 class=\"faq-q\">Quelle est la diff\u00e9rence entre l&#039;IA et l&#039;apprentissage automatique dans le domaine a\u00e9rospatial ?<\/h3>\n<div>\n<p class=\"faq-a\">L&#039;intelligence artificielle (IA) est la discipline plus vaste qui consiste \u00e0 cr\u00e9er des syst\u00e8mes informatiques imitant les capacit\u00e9s intelligentes humaines\u00a0: percevoir, d\u00e9cider et agir. L&#039;apprentissage automatique (ML), sous-ensemble essentiel de l&#039;IA, utilise des m\u00e9thodes informatiques pour entra\u00eener des mod\u00e8les en apprenant \u00e0 partir de donn\u00e9es plut\u00f4t qu&#039;en suivant des r\u00e8gles explicitement programm\u00e9es. Dans le domaine a\u00e9rospatial, le ML fournit le m\u00e9canisme d&#039;apprentissage qui alimente les syst\u00e8mes d&#039;IA.<\/p>\n<\/div>\n<\/div>\n<div class=\"faq-question\">\n<h3 class=\"faq-q\">L\u2019apprentissage automatique remplace-t-il les ing\u00e9nieurs a\u00e9rospatiaux\u00a0?<\/h3>\n<div>\n<p class=\"faq-a\">Absolument pas. L&#039;apprentissage automatique (ML) enrichit les comp\u00e9tences des ing\u00e9nieurs au lieu de les remplacer. Les ing\u00e9nieurs utilisent les outils de ML pour explorer des espaces de conception plus vastes, traiter davantage de donn\u00e9es et prendre des d\u00e9cisions plus \u00e9clair\u00e9es. Cette technologie prend en charge les t\u00e2ches d&#039;analyse r\u00e9p\u00e9titives et la reconnaissance de formes, permettant ainsi aux ing\u00e9nieurs de se concentrer sur la r\u00e9solution cr\u00e9ative de probl\u00e8mes, la planification strat\u00e9gique et l&#039;innovation, activit\u00e9s qui requi\u00e8rent le jugement humain et l&#039;expertise du domaine.<\/p>\n<h2><span style=\"font-weight: 400;\">Conclusion<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">L&#039;apprentissage automatique est pass\u00e9 de la recherche exp\u00e9rimentale aux infrastructures a\u00e9rospatiales critiques. Cette technologie prouve quotidiennement sa valeur, des rovers explorant le terrain martien aux avions commerciaux optimisant leurs programmes de maintenance.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Mais ce n&#039;est qu&#039;un d\u00e9but. \u00c0 mesure que les cadres r\u00e9glementaires se perfectionnent, que les capacit\u00e9s de calcul augmentent et que les ensembles de donn\u00e9es s&#039;enrichissent, les applications d&#039;apprentissage automatique dans le secteur a\u00e9rospatial ne feront que s&#039;acc\u00e9l\u00e9rer.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">L&#039;industrie qui a permis \u00e0 l&#039;humanit\u00e9 de voler, de voyager \u00e0 des vitesses supersoniques et d&#039;explorer l&#039;espace exploite d\u00e9sormais l&#039;apprentissage automatique pour repousser encore plus loin les limites. Et les r\u00e9sultats sont plus \u00e9loquents que n&#039;importe quelle pr\u00e9diction.<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Quick Summary: Machine learning is revolutionizing aerospace through autonomous spacecraft navigation, predictive maintenance, and optimized aircraft design. NASA&#8217;s Perseverance rover demonstrates 88% autonomous driving using ML terrain analysis, while regulatory bodies like EASA and FAA establish frameworks for AI trustworthiness in aviation. From manufacturing efficiency to safety improvements, ML applications span the entire aerospace lifecycle, [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":36813,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[1],"tags":[],"class_list":["post-36812","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Machine Learning in Aerospace: 2026 Industry Guide<\/title>\n<meta name=\"description\" content=\"Discover how machine learning transforms aerospace\u2014from autonomous spacecraft to predictive maintenance. Real NASA data, EASA regulations, and practical applications.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/aisuperior.com\/fr\/machine-learning-in-aerospace-industry\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning in Aerospace: 2026 Industry Guide\" \/>\n<meta property=\"og:description\" content=\"Discover how machine learning transforms aerospace\u2014from autonomous spacecraft to predictive maintenance. Real NASA data, EASA regulations, and practical applications.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/aisuperior.com\/fr\/machine-learning-in-aerospace-industry\/\" \/>\n<meta property=\"og:site_name\" content=\"aisuperior\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/aisuperior\" \/>\n<meta property=\"article:published_time\" content=\"2026-05-20T11:04:41+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/aisuperior.com\/wp-content\/uploads\/2026\/05\/unnamed-1-7.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1168\" \/>\n\t<meta property=\"og:image:height\" content=\"784\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"kateryna\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@aisuperior\" \/>\n<meta name=\"twitter:site\" content=\"@aisuperior\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"kateryna\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"8 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/aisuperior.com\\\/machine-learning-in-aerospace-industry\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/aisuperior.com\\\/machine-learning-in-aerospace-industry\\\/\"},\"author\":{\"name\":\"kateryna\",\"@id\":\"https:\\\/\\\/aisuperior.com\\\/#\\\/schema\\\/person\\\/14fcb7aaed4b2b617c4f75699394241c\"},\"headline\":\"Machine Learning in Aerospace: 2026 Industry Guide\",\"datePublished\":\"2026-05-20T11:04:41+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/aisuperior.com\\\/machine-learning-in-aerospace-industry\\\/\"},\"wordCount\":1540,\"publisher\":{\"@id\":\"https:\\\/\\\/aisuperior.com\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/aisuperior.com\\\/machine-learning-in-aerospace-industry\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/aisuperior.com\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/unnamed-1-7.webp\",\"articleSection\":[\"Blog\"],\"inLanguage\":\"fr-FR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/aisuperior.com\\\/machine-learning-in-aerospace-industry\\\/\",\"url\":\"https:\\\/\\\/aisuperior.com\\\/machine-learning-in-aerospace-industry\\\/\",\"name\":\"Machine Learning in Aerospace: 2026 Industry Guide\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/aisuperior.com\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/aisuperior.com\\\/machine-learning-in-aerospace-industry\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/aisuperior.com\\\/machine-learning-in-aerospace-industry\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/aisuperior.com\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/unnamed-1-7.webp\",\"datePublished\":\"2026-05-20T11:04:41+00:00\",\"description\":\"Discover how machine learning transforms aerospace\u2014from autonomous spacecraft to predictive maintenance. Real NASA data, EASA regulations, and practical applications.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/aisuperior.com\\\/machine-learning-in-aerospace-industry\\\/#breadcrumb\"},\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/aisuperior.com\\\/machine-learning-in-aerospace-industry\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\\\/\\\/aisuperior.com\\\/machine-learning-in-aerospace-industry\\\/#primaryimage\",\"url\":\"https:\\\/\\\/aisuperior.com\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/unnamed-1-7.webp\",\"contentUrl\":\"https:\\\/\\\/aisuperior.com\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/unnamed-1-7.webp\",\"width\":1168,\"height\":784},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/aisuperior.com\\\/machine-learning-in-aerospace-industry\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/aisuperior.com\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Machine Learning in Aerospace: 2026 Industry Guide\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/aisuperior.com\\\/#website\",\"url\":\"https:\\\/\\\/aisuperior.com\\\/\",\"name\":\"aisuperior\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\\\/\\\/aisuperior.com\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/aisuperior.com\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/aisuperior.com\\\/#organization\",\"name\":\"aisuperior\",\"url\":\"https:\\\/\\\/aisuperior.com\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\\\/\\\/aisuperior.com\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/aisuperior.com\\\/wp-content\\\/uploads\\\/2026\\\/02\\\/logo-1.png.webp\",\"contentUrl\":\"https:\\\/\\\/aisuperior.com\\\/wp-content\\\/uploads\\\/2026\\\/02\\\/logo-1.png.webp\",\"width\":320,\"height\":59,\"caption\":\"aisuperior\"},\"image\":{\"@id\":\"https:\\\/\\\/aisuperior.com\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.facebook.com\\\/aisuperior\",\"https:\\\/\\\/x.com\\\/aisuperior\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/ai-superior\",\"https:\\\/\\\/www.instagram.com\\\/ai_superior\\\/\"]},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/aisuperior.com\\\/#\\\/schema\\\/person\\\/14fcb7aaed4b2b617c4f75699394241c\",\"name\":\"kateryna\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\\\/\\\/aisuperior.com\\\/wp-content\\\/litespeed\\\/avatar\\\/6c451fec1b37608859459eb63b5a3380.jpg?ver=1781011836\",\"url\":\"https:\\\/\\\/aisuperior.com\\\/wp-content\\\/litespeed\\\/avatar\\\/6c451fec1b37608859459eb63b5a3380.jpg?ver=1781011836\",\"contentUrl\":\"https:\\\/\\\/aisuperior.com\\\/wp-content\\\/litespeed\\\/avatar\\\/6c451fec1b37608859459eb63b5a3380.jpg?ver=1781011836\",\"caption\":\"kateryna\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Apprentissage automatique dans l&#039;a\u00e9rospatiale : guide de l&#039;industrie 2026","description":"D\u00e9couvrez comment l&#039;apprentissage automatique transforme l&#039;a\u00e9rospatiale, des engins spatiaux autonomes \u00e0 la maintenance pr\u00e9dictive. Donn\u00e9es r\u00e9elles de la NASA, r\u00e9glementations de l&#039;AESA et applications pratiques.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/aisuperior.com\/fr\/machine-learning-in-aerospace-industry\/","og_locale":"fr_FR","og_type":"article","og_title":"Machine Learning in Aerospace: 2026 Industry Guide","og_description":"Discover how machine learning transforms aerospace\u2014from autonomous spacecraft to predictive maintenance. Real NASA data, EASA regulations, and practical applications.","og_url":"https:\/\/aisuperior.com\/fr\/machine-learning-in-aerospace-industry\/","og_site_name":"aisuperior","article_publisher":"https:\/\/www.facebook.com\/aisuperior","article_published_time":"2026-05-20T11:04:41+00:00","og_image":[{"width":1168,"height":784,"url":"https:\/\/aisuperior.com\/wp-content\/uploads\/2026\/05\/unnamed-1-7.webp","type":"image\/webp"}],"author":"kateryna","twitter_card":"summary_large_image","twitter_creator":"@aisuperior","twitter_site":"@aisuperior","twitter_misc":{"\u00c9crit par":"kateryna","Dur\u00e9e de lecture estim\u00e9e":"8 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/aisuperior.com\/machine-learning-in-aerospace-industry\/#article","isPartOf":{"@id":"https:\/\/aisuperior.com\/machine-learning-in-aerospace-industry\/"},"author":{"name":"kateryna","@id":"https:\/\/aisuperior.com\/#\/schema\/person\/14fcb7aaed4b2b617c4f75699394241c"},"headline":"Machine Learning in Aerospace: 2026 Industry Guide","datePublished":"2026-05-20T11:04:41+00:00","mainEntityOfPage":{"@id":"https:\/\/aisuperior.com\/machine-learning-in-aerospace-industry\/"},"wordCount":1540,"publisher":{"@id":"https:\/\/aisuperior.com\/#organization"},"image":{"@id":"https:\/\/aisuperior.com\/machine-learning-in-aerospace-industry\/#primaryimage"},"thumbnailUrl":"https:\/\/aisuperior.com\/wp-content\/uploads\/2026\/05\/unnamed-1-7.webp","articleSection":["Blog"],"inLanguage":"fr-FR"},{"@type":"WebPage","@id":"https:\/\/aisuperior.com\/machine-learning-in-aerospace-industry\/","url":"https:\/\/aisuperior.com\/machine-learning-in-aerospace-industry\/","name":"Apprentissage automatique dans l&#039;a\u00e9rospatiale : guide de l&#039;industrie 2026","isPartOf":{"@id":"https:\/\/aisuperior.com\/#website"},"primaryImageOfPage":{"@id":"https:\/\/aisuperior.com\/machine-learning-in-aerospace-industry\/#primaryimage"},"image":{"@id":"https:\/\/aisuperior.com\/machine-learning-in-aerospace-industry\/#primaryimage"},"thumbnailUrl":"https:\/\/aisuperior.com\/wp-content\/uploads\/2026\/05\/unnamed-1-7.webp","datePublished":"2026-05-20T11:04:41+00:00","description":"D\u00e9couvrez comment l&#039;apprentissage automatique transforme l&#039;a\u00e9rospatiale, des engins spatiaux autonomes \u00e0 la maintenance pr\u00e9dictive. Donn\u00e9es r\u00e9elles de la NASA, r\u00e9glementations de l&#039;AESA et applications pratiques.","breadcrumb":{"@id":"https:\/\/aisuperior.com\/machine-learning-in-aerospace-industry\/#breadcrumb"},"inLanguage":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/aisuperior.com\/machine-learning-in-aerospace-industry\/"]}]},{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/aisuperior.com\/machine-learning-in-aerospace-industry\/#primaryimage","url":"https:\/\/aisuperior.com\/wp-content\/uploads\/2026\/05\/unnamed-1-7.webp","contentUrl":"https:\/\/aisuperior.com\/wp-content\/uploads\/2026\/05\/unnamed-1-7.webp","width":1168,"height":784},{"@type":"BreadcrumbList","@id":"https:\/\/aisuperior.com\/machine-learning-in-aerospace-industry\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/aisuperior.com\/"},{"@type":"ListItem","position":2,"name":"Machine Learning in Aerospace: 2026 Industry Guide"}]},{"@type":"WebSite","@id":"https:\/\/aisuperior.com\/#website","url":"https:\/\/aisuperior.com\/","name":"aisuperior","description":"","publisher":{"@id":"https:\/\/aisuperior.com\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/aisuperior.com\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"fr-FR"},{"@type":"Organization","@id":"https:\/\/aisuperior.com\/#organization","name":"aisuperior","url":"https:\/\/aisuperior.com\/","logo":{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/aisuperior.com\/#\/schema\/logo\/image\/","url":"https:\/\/aisuperior.com\/wp-content\/uploads\/2026\/02\/logo-1.png.webp","contentUrl":"https:\/\/aisuperior.com\/wp-content\/uploads\/2026\/02\/logo-1.png.webp","width":320,"height":59,"caption":"aisuperior"},"image":{"@id":"https:\/\/aisuperior.com\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/aisuperior","https:\/\/x.com\/aisuperior","https:\/\/www.linkedin.com\/company\/ai-superior","https:\/\/www.instagram.com\/ai_superior\/"]},{"@type":"Person","@id":"https:\/\/aisuperior.com\/#\/schema\/person\/14fcb7aaed4b2b617c4f75699394241c","name":"Katerina","image":{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/aisuperior.com\/wp-content\/litespeed\/avatar\/6c451fec1b37608859459eb63b5a3380.jpg?ver=1781011836","url":"https:\/\/aisuperior.com\/wp-content\/litespeed\/avatar\/6c451fec1b37608859459eb63b5a3380.jpg?ver=1781011836","contentUrl":"https:\/\/aisuperior.com\/wp-content\/litespeed\/avatar\/6c451fec1b37608859459eb63b5a3380.jpg?ver=1781011836","caption":"kateryna"}}]}},"_links":{"self":[{"href":"https:\/\/aisuperior.com\/fr\/wp-json\/wp\/v2\/posts\/36812","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aisuperior.com\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aisuperior.com\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aisuperior.com\/fr\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/aisuperior.com\/fr\/wp-json\/wp\/v2\/comments?post=36812"}],"version-history":[{"count":1,"href":"https:\/\/aisuperior.com\/fr\/wp-json\/wp\/v2\/posts\/36812\/revisions"}],"predecessor-version":[{"id":36814,"href":"https:\/\/aisuperior.com\/fr\/wp-json\/wp\/v2\/posts\/36812\/revisions\/36814"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aisuperior.com\/fr\/wp-json\/wp\/v2\/media\/36813"}],"wp:attachment":[{"href":"https:\/\/aisuperior.com\/fr\/wp-json\/wp\/v2\/media?parent=36812"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aisuperior.com\/fr\/wp-json\/wp\/v2\/categories?post=36812"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aisuperior.com\/fr\/wp-json\/wp\/v2\/tags?post=36812"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}