8 papers · 1 filter
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…
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…
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…
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…
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…