9 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…
Q-SINDy: Quantum-Kernel Sparse Identification of Nonlinear Dynamics with Provable Coefficient Debiasing
Samrendra Roy, Syed Bahauddin Alam
Quantum feature maps offer expressive embeddings for classical learning tasks, and augmenting sparse identification of nonlinear dynamics (SINDy) with such features is a natural bu…
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,…
Beyond Uniform Sampling: Synergistic Active Learning and Input Denoising for Robust Neural Operators
Samrendra Roy, Souvik Chakraborty, Syed Bahauddin Alam
Neural operators have emerged as fast surrogate models for physics simulations, yet they remain acutely vulnerable to adversarial perturbations, a critical liability for safety-cri…
Neuromorphic Continual Learning for Sequential Deployment of Nuclear Plant Monitoring Systems
Samrendra Roy, Sajedul Talukder, Syed Bahauddin Alam
Anomaly detection in nuclear industrial control systems (ICS) requires continuous, energy-efficient monitoring across multiple subsystems that are often deployed at different stage…
SCNO: Spiking Compositional Neural Operator -- Towards a Neuromorphic Foundation Model for Nuclear PDE Solving
Samrendra Roy, Souvik Chakraborty, Rizwan-uddin +1
Neural operators have emerged as powerful surrogates for partial differential equation (PDE) solvers, yet they are typically trained as monolithic models for individual PDEs, requi…
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…
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…
Spatiotemporal Reconstruction of Global Radiation from Sparse Sensor Sequences
Kazuma Kobayashi, Tapas Tripura, Jay Phil Yoo +5
Much of the physical world is inaccessible to direct measurement and can be reached only through the sparse traces it leaves in other observable variables. Here we introduce the Te…