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cs.LG2025
Event-driven physics-informed operator learning for reliability analysis
Shailesh Garg, Souvik Chakraborty
Reliability analysis of engineering systems under uncertainty poses significant computational challenges, particularly for problems involving high-dimensional stochastic inputs, no…
physics.comp-ph2025
NeuroPINNs: Neuroscience Inspired Physics Informed Neural Networks
Shailesh Garg, Souvik Chakraborty
We introduce NeuroPINNs, a neuroscience-inspired extension of Physics-Informed Neural Networks (PINNs) that incorporates biologically motivated spiking neuron models to achieve ene…
cs.LG2025
Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators
Kazuma Kobayashi, Shailesh Garg, Farid Ahmed +2
Robust uncertainty quantification (UQ) remains a critical barrier to the safe deployment of deep learning in real-time virtual sensing, particularly in high-stakes domains where sp…