OrthoReg applies orthogonal regularization to enforce complementary decomposition in hybrid symbolic-neural dynamical models, improving symbolic recovery and out-of-distribution performance on benchmarks with partial library mismatch.
arXiv preprint arXiv:2211.08064 , year=
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Hybrid GNN-FEM surrogate replaces only the phase-field update step in a staggered incremental scheme for phase-field fracture, using dimensionless mesh features and physics-informed loss to generalize across geometries, loads, materials, and discretizations.
A Creator-Inspector multi-agent LLM pipeline for constitutive artificial neural networks increases the rate of models satisfying all nine physical constraints to 100% or 56% depending on the LLM backbone.
AOT-POT adaptively reshapes complex PDE solution operators via input-dependent transformations and parallel stream mixing to enable effective large-scale pre-training, yielding SOTA results on 12 benchmarks with minimal added parameters.
ST-PT turns transformers into explicit factor graphs for time series, enabling structural injection of symbolic priors, per-sample conditional generation, and principled latent autoregressive forecasting via MFVI iterations.
MH-PINN compactifies unbounded domains with mapping and enforces wave boundary conditions through network architecture for efficient, accurate simulations.
A physics-informed MLP reconstructs high-fidelity 4D spectra from only 1/32 of the samples in experimental 2DIR hyperspectral imaging.
Graph-based summary statistics on pulsar timing residuals detect SGWB down to strain amplitude 1.2e-15 and yield 2.3 sigma evidence in NANOGrav 15-year data via clustering coefficient and edge weight measures.
Introduces Laplace-approximated Bayesian PINNs for automatic loss-weight optimization when solving PDEs such as heat, wave, and Burgers equations.
PINNSur applies PINNs to surface PDEs by neural approximation of normals and operator projection, with an added empirical test for convergence behavior.
A scoping review of physics-informed machine learning for seismic wave propagation finds applications in forward and inverse problems with often comparable accuracy at lower cost, while identifying gaps in benchmarking, training cost, and 3D/experimental validation.
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Bayesian Reasoning for Physics Informed Neural Networks
Introduces Laplace-approximated Bayesian PINNs for automatic loss-weight optimization when solving PDEs such as heat, wave, and Burgers equations.