8 papers
When Spike Sparsity Does Not Translate to Deployed Cost: VS-WNO on Jetson Orin Nano
Jason Yoo, Shailesh Garg, Souvik Chakraborty +1
Spiking neural operators are appealing for neuromorphic edge computing because event-driven substrates can, in principle, translate sparse activity into lower latency and energy. W…
CoNBONet: Conformalized Neuroscience-inspired Bayesian Operator Network for Reliability Analysis
Shailesh Garg, Souvik Chakraborty
Time-dependent reliability analysis of nonlinear dynamical systems under stochastic excitations is a critical yet computationally demanding task. Conventional approaches, such as M…
SPINONet: Scalable Spiking Physics-informed Neural Operator for Computational Mechanics Applications
Shailesh Garg, Luis Mandl, Somdatta Goswami +1
Energy efficiency remains a critical challenge in deploying physics-informed operator learning models for computational mechanics and scientific computing, particularly in power-co…
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…
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…
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…