16 citations · 33 across the 8 of their papers we have counts for
8 papers
Oscillatory State-Space Models as Inductive Biases for Physics-Informed Neural PDE Solvers
Abhishek Chandra, Taniya Kapoor
Solving time-dependent partial differential equations (PDEs) is an important problem in computational science and engineering. Physics-informed neural networks (PINNs) learn PDE so…
Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems
Abhishek Chandra, Taniya Kapoor, Mitrofan Curti +2
Complex piezoelectric systems are foundational in industrial applications. Their performance, however, is challenged by the nonlinear voltage-displacement hysteretic relationships.…
Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers
Taniya Kapoor, Abhishek Chandra, Anastasios Stamou +1
Real-world systems, from aerospace to railway engineering, are modeled with partial differential equations (PDEs) describing the physics of the system. Estimating robust solutions…
Magnetic Hysteresis Modeling with Neural Operators
Abhishek Chandra, Bram Daniels, Mitrofan Curti +2
Hysteresis modeling is crucial to comprehend the behavior of magnetic devices, facilitating optimal designs. Hitherto, deep learning-based methods employed to model hysteresis, fac…
Fast training of accurate physics-informed neural networks without gradient descent
Chinmay Datar, Taniya Kapoor, Abhishek Chandra +6
Solving time-dependent Partial Differential Equations (PDEs) is one of the most critical problems in computational science. While Physics-Informed Neural Networks (PINNs) offer a p…
Neural oscillators for magnetic hysteresis modeling
Abhishek Chandra, Taniya Kapoor, Bram Daniels +4
Hysteresis is a ubiquitous phenomenon in science and engineering; its modeling and identification are crucial for understanding and optimizing the behavior of various systems. We d…
Neural oscillators for generalization of physics-informed machine learning
Taniya Kapoor, Abhishek Chandra, Daniel M. Tartakovsky +3
A primary challenge of physics-informed machine learning (PIML) is its generalization beyond the training domain, especially when dealing with complex physical problems represented…
Discovery of sparse hysteresis models for piezoelectric materials
Abhishek Chandra, Bram Daniels, Mitrofan Curti +3
This article presents an approach for modelling hysteresis in piezoelectric materials, that leverages recent advancements in machine learning, particularly in sparse-regression tec…