14 papers
Targeted search shows that random-device testing underestimates worst-case error in a simulated wave-based neural operator
Samrendra Roy, Jason Yoo, Souvik Chakraborty +1
Wave-based processors promise fast, energy-efficient Fourier layers for neural operators. They are usually validated on randomly sampled devices, but using them requires knowing ho…
Stabilizing Autoregressive PDE Foundation Model Rollouts with Event-Triggered Context Healing
Jaewan Park, Jay Phil Yoo, Kazuma Kobayashi +5
Autoregressive PDE foundation models enable fast full-field forecasting but accumulate errors as predicted states are recursively reused. We introduce Event-Triggered Context Heali…
Energy-efficient operation of neural operators for virtual sensing
Jason Yoo, Samrendra Roy, Souvik Chakraborty +1
Virtual sensing repeatedly reconstructs physical fields from changing observations, often on a fixed geometry. We investigate how shared spatial computation reduces the energy of t…
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…
Neuroscience Inspired Graph Operators Towards Edge-Deployable Virtual Sensing for Irregular Geometries
William Howes, Farid Ahmed, Kazuma Kobayashi +2
Predicting full-field physics through the real-time virtual sensing of engineering systems can enhance limited physical sensors but often requires sparse-to-dense reconstruction, c…
Gradient-Free Continual Learning in Spiking Neural Networks via Inter-Spike Interval Regularization
Samrendra Roy, Kazuma Kobayashi, Souvik Chakraborty +2
Continual learning, the ability to acquire new tasks sequentially without forgetting prior knowledge, is essential for deploying neural networks in dynamic real-world environments,…
Hardware-Software Co-Design for Event-Driven SNN Deployment on Low-Cost Neuromorphic FPGAs
Jiwoon Lee, Souvik Chakraborty, Syed Bahauddin Alam +1
Low-cost FPGA platforms can broaden access to neuromorphic systems research, but current spiking neural network (SNN) workflows remain divided between hardware-first implementation…
Adversarial Vulnerabilities in Neural Operator Digital Twins: Gradient-Free Attacks on Nuclear Thermal-Hydraulic Surrogates
Samrendra Roy, Kazuma Kobayashi, Souvik Chakraborty +2
Operator learning models are rapidly emerging as the predictive core of digital twins for nuclear and energy systems, promising real-time field reconstruction from sparse sensor me…
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…
TrustFed: Enabling Trustworthy Medical AI under Data Privacy Constraints
Vagish Kumar, Syed Bahauddin Alam, Souvik Chakraborty
Protecting patient privacy remains a fundamental barrier to scaling machine learning across healthcare institutions, where centralizing sensitive data is often infeasible due to et…
Agentic Physical AI toward a Domain-Specific Foundation Model for Energy Systems: A Case Study on Nuclear Reactor Control
Yoon Pyo Lee, Samrendra Roy, Kazuma Kobayashi +5
The prevailing paradigm in AI for physical systems: scaling general-purpose foundation models toward universal multimodal reasoning, confronts a barrier at the control interface. F…
Sensing Without Colocation: Operator-Based Virtual Instrumentation for Domains Beyond Physical Reach
Jay Phil Yoo, Kazuma Kobayashi, Souvik Chakraborty +1
Classical sensing rests on one foundational assumption: the quantity of interest must be colocated with the measurement device. This is not an engineering convenience. It is the or…
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…