InfoAtlas is a pretrained neural model for zero-shot mutual information estimation that matches state-of-the-art accuracy with 100x speedup and handles varying dimensions via a single model.
Simulation intelligence: Towards a new generation of scientific methods.arXiv preprint arXiv:2112.03235, 2021
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Courant is a state-adaptive Perceiver encoder-processor-decoder surrogate trained with L2 loss that yields interpretable, multiscale, locally supported latent features acting as time-evolving spatial basis functions.
Novel operator-splitting PINN forms for higher-level PDAEs are introduced and assessed against DeepXDE, with a custom JAX implementation showing advantages in avoiding certain pathologies.
Porting AI-accelerated CFD model training to IPU-POD16 yields 34% data-feeding speedup and scales throughput to 2805 samples/s on 16 IPUs despite inter-IPU communication limits.
A comprehensive review of deep learning techniques for computational mechanics, including LSTM for constitutive modeling, PINNs for PDE solving, optimizers, and kernel methods.
citing papers explorer
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InfoAtlas: A Foundation Model for Zero-Shot Statistical Dependence Estimate
InfoAtlas is a pretrained neural model for zero-shot mutual information estimation that matches state-of-the-art accuracy with 100x speedup and handles varying dimensions via a single model.
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Courant: a State-Adaptive Perceiver-Based Neural Surrogate with Local Support and Interpretable Field Decomposition
Courant is a state-adaptive Perceiver encoder-processor-decoder surrogate trained with L2 loss that yields interpretable, multiscale, locally supported latent features acting as time-evolving spatial basis functions.
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Partial-differential-algebraic equations of nonlinear dynamics by Physics-Informed Neural-Network: (I) Operator splitting and framework assessment
Novel operator-splitting PINN forms for higher-level PDAEs are introduced and assessed against DeepXDE, with a custom JAX implementation showing advantages in avoiding certain pathologies.
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Adaptation of AI-accelerated CFD Simulations to the IPU platform
Porting AI-accelerated CFD model training to IPU-POD16 yields 34% data-feeding speedup and scales throughput to 2805 samples/s on 16 IPUs despite inter-IPU communication limits.
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Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics
A comprehensive review of deep learning techniques for computational mechanics, including LSTM for constitutive modeling, PINNs for PDE solving, optimizers, and kernel methods.