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06/15/2023
PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs
While significant progress has been made on Physics-Informed Neural Netw...
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06/05/2023
MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks
Physics-informed Neural Networks (PINNs) have recently achieved remarkab...
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05/30/2023
NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data
The neural operator has emerged as a powerful tool in learning mappings ...
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03/09/2023
Reward Informed Dreamer for Task Generalization in Reinforcement Learning
A long-standing goal of reinforcement learning is that algorithms can le...
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02/28/2023
GNOT: A General Neural Operator Transformer for Operator Learning
Learning partial differential equations' (PDEs) solution operators is an...
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11/15/2022
Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications
Recent advances of data-driven machine learning have revolutionized fiel...
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10/06/2022
A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs
We present a unified hard-constraint framework for solving geometrically...
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09/30/2022
Equivariant Energy-Guided SDE for Inverse Molecular Design
Inverse molecular design is critical in material science and drug discov...
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09/15/2022
Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients
Deep learning based approaches like Physics-informed neural networks (PI...
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09/15/2022
On the Reuse Bias in Off-Policy Reinforcement Learning
Importance sampling (IS) is a popular technique in off-policy evaluation...
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06/09/2022
GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing
Certified defenses such as randomized smoothing have shown promise towar...
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09/27/2021
Query-based Adversarial Attacks on Graph with Fake Nodes
While deep neural networks have achieved great success on the graph anal...
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07/07/2020
