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              <p>&lt;div&gt;&lt;p&gt;This paper describes neural simulation of the scattered pressure field from a plane wave around a scattering object in both continuous 2D and 3D domains. This task has typically been treated as a regression problem that aims to train a physicsinformed neural network (PINN) using pressure measurements at discrete positions. This approach, however, needs to train the whole network for each incident wave direction. To address this, we propose a measurement-free simulator based on a PINN purely driven by the Helmholtz equation with the Robin boundary condition and the Sommerfeld radiation condition with the aid of the perfectly matched layer (PML) framework. More specifically, we design a physics-informed scattering hypernetwork (PHISK) that can generalize to incident waves from any direction via low-rank adaptation (LoRA) of a PINN trained for a specific configuration. The experiment shows that the proposed method accurately simulated sound scattering around various objects, adapting to unseen incident wave directions with minimal performance loss, and realized reasonable simulation of head-related transfer functions (HRTFs) from complex mesh data of a human head.&lt;/p&gt;&lt;/div&gt;</p>
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              <p>Cet article décrit la simulation neuronale du champ de pression diffusée à partir d'une onde plane autour d'un objet diffusant de façon continue à la fois en 2D ou en 3D. Cette tâche a généralement été traitée comme un problème de régression visant à entraîner un réseau de neurones informé par la physique (PINN) à l'aide de mesures acoustiques de la pression à partir d'un nombre fini de positions. Cependant, cette approche nécessite d'entraîner l'ensemble du réseau pour chaque direction d'onde incidente simulée. Pour y remédier, nous proposons un simulateur sans mesure basé sur un PINN purement guidé par l'équation de Helmholtz avec la condition aux limites de Robin et la condition de rayonnement de Sommerfeld, assisté par le cadre de la théorie du 'Perfectly Matched Layer' (PML). Plus précisément, nous concevons un hyperréseau de diffusion informé par la physique nommé PHISK capable de généraliser aux ondes incidentes provenant de n'importe quelle direction via l'adaptation de rang faible (LoRA) d'un PINN entraîné pour une configuration spécifique. L'expérience montre que la méthode proposée simule avec précision la diffusion sonore autour de divers objets, s'adaptant à des directions d'ondes incidentes non vues avec une perte de performance minimale, et réalise une simulation cohérente des fonctions de transfert relatives à la tête (HRTF) à partir de données de maillage complexes d'une tête humaine.</p>
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        <org type="anrProject" xml:id="projanr-66316" status="VALID">
          <idno type="anr">ANR-22-CE23-0011</idno>
          <orgName>SAROUMANE</orgName>
          <desc>Segmentation et regroupement de locuteurs via un modèle robuste unifié audio spatial et multimodal</desc>
          <date type="start">2022</date>
        </org>
      </listOrg>
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</TEI>