12 citations · 23 across the 5 of their papers we have counts for
6 papers
Closed-Form Optimal Weight Splitting for Matrix-Vector Multiply on Noisy In-Memory Accelerators
Mehmet Aktukmak, David A. Roberts
Analog in-memory computing performs the matrix-vector multiplications of neural networks directly inside the memory array, which saves time and energy. But each memory cell stores…
Hard-constrained Physics-informed Neural Networks for Interface Problems
Seung Whan Chung, Stephen T. Castonguay, Sumanta Roy +3
Physics-informed neural networks (PINNs) have emerged as a flexible framework for solving partial differential equations, but interface problems remain challenging because continui…
DeepONet: A Discontinuity Capturing Neural Operator
Sumanta Roy, Stephen T. Castonguay, Pratanu Roy +1
We present DeepONet, a physics-informed neural operator designed to learn mappings between function spaces that may contain discontinuities or exhibit non-smooth behavior. Clas…
ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning
Wenqian Chen, Zhi-Feng Wei, Yucheng Fu +3
Learning solution operators on arbitrary geometries remains a central challenge in scientific machine learning, especially for many-query simulation, physics-informed learning, and…
Physics-informed Neural Networks for Heterogeneous Poroelastic Media
Sumanta Roy, Chandrasekhar Annavarapu, Pratanu Roy +1
This study presents a novel physics-informed neural network (PINN) framework for modeling poroelasticity in heterogeneous media with material interfaces. The approach introduces a…
Adaptive Interface-PINNs (AdaI-PINNs): An Efficient Physics-informed Neural Networks Framework for Interface Problems
Sumanta Roy, Chandrasekhar Annavarapu, Pratanu Roy +1
We present an efficient physics-informed neural networks (PINNs) framework, termed Adaptive Interface-PINNs (AdaI-PINNs), to improve the modeling of interface problems with discont…