FOSSA scores sensor importance for PINN inverse problems via first-order optimality conditions at convergence and shows that low-importance sensors can degrade reconstruction accuracy in electrocardiographic imaging.
Physics-informed neural networks and extensions
4 Pith papers cite this work. Polarity classification is still indexing.
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A physics-informed Fourier-wavelet transformer model reports the lowest normalized mean-squared error on cylinder-wake and fluid-structure interaction velocity-field benchmarks compared with spectral, transformer, operator-learning, and PINN baselines.
A two-level HPRNN framework is proposed that embeds physical properties into latent spaces to surrogate nonlinear elasto-plastic yarn behavior and meso-to-macro transitions for woven composites.
On synthetic CSBM graphs, a graph-distance bias knob systematically shifts Graph Transformers between over-globalizing and under-reaching regimes, and an oracle target-gap controller tracks the best fixed bias across task localities.
citing papers explorer
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Computational Control of Nonlinear Partial Differential Equations Using Machine Learning
On synthetic CSBM graphs, a graph-distance bias knob systematically shifts Graph Transformers between over-globalizing and under-reaching regimes, and an oracle target-gap controller tracks the best fixed bias across task localities.