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Δ-PINNs: Physics-informed neural networks on complex geometries.

, , and . Eng. Appl. Artif. Intell., 127 (Part B): 107324 (January 2024)

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Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps., , , , , and . Eng. Comput., 38 (5): 3957-3973 (2022)Δ-PINNs: physics-informed neural networks on complex geometries., , and . CoRR, (2022)Δ-PINNs: Physics-informed neural networks on complex geometries., , and . Eng. Appl. Artif. Intell., 127 (Part B): 107324 (January 2024)Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields., , , , and . CoRR, (2023)Learning Atrial Fiber Orientations and Conductivity Tensors from Intracardiac Maps Using Physics-Informed Neural Networks., , , , , , and . FIMH, volume 12738 of Lecture Notes in Computer Science, page 650-658. Springer, (2021)Benchmarks for physics-informed data-driven hyperelasticity., , , , and . CoRR, (2023)Understanding the dynamics of the frequency bias in neural networks., , , and . CoRR, (2024)Physics-informed neural networks for parameter estimation in blood flow models., , , and . Comput. Biol. Medicine, (2024)Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models., , , and . CoRR, (2019)Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations., , , and . CoRR, (2023)