arXiv Machine Learning
Latent-MoE: Domain-Aware Mixture-of-Experts for PDEs with Multi-Regime Physics
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LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks
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Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing
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Multi-dimensional training-priority weighting based on physical information propagation paths: a unified residual-weighting framework for physics-informed neural networks
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arXiv Machine Learning
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Tackling Failure Modes of PINNs and PIKANs Using Conflict-Free Gradients
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arXiv Machine Learning
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On the training of physics-informed neural operators for solving parametric partial differential equations
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arXiv Machine Learning
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Architecture--Optimization Co-Design for Physics-Informed Neural Networks via Layer-wise Coordinate Adaptation and Gradient Conflict Resolution
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