100 citations · 184 across the 9 of their papers we have counts for
11 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…
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…
SUPN: Shallow Universal Polynomial Networks for Efficient Function Approximation
Zachary Morrow, Michael Penwarden, Brian Chen +3
Deep neural networks (DNNs) and Kolmogorov-Arnold networks (KANs) are flexible function approximators, but their accuracy can require substantial network depth, width, and trainabl…
Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning (PIML) Methods: Towards Robust Metrics
Michael Penwarden, Houman Owhadi, Robert M. Kirby
Physics-informed machine learning (PIML) as a means of solving partial differential equations (PDE) has garnered much attention in the Computational Science and Engineering (CS&E)…
Neural Operator Learning for Ultrasound Tomography Inversion
Haocheng Dai, Michael Penwarden, Robert M. Kirby +1
Neural operator learning as a means of mapping between complex function spaces has garnered significant attention in the field of computational science and engineering (CS&E). In t…
Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils
Khemraj Shukla, Vivek Oommen, Ahmad Peyvan +5
Deep neural operators, such as DeepONets, have changed the paradigm in high-dimensional nonlinear regression from function regression to (differential) operator regression, paving…
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions
Michael Penwarden, Ameya D. Jagtap, Shandian Zhe +2
Physics-informed neural networks (PINNs) as a means of solving partial differential equations (PDE) have garnered much attention in the Computational Science and Engineering (CS&E)…
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks
Shibo Li, Michael Penwarden, Yiming Xu +4
Physics-informed neural networks (PINNs) are emerging as popular mesh-free solvers for partial differential equations (PDEs). Recent extensions decompose the domain, apply differen…
A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs
Michael Penwarden, Shandian Zhe, Akil Narayan +1
Physics-informed neural networks (PINNs) as a means of discretizing partial differential equations (PDEs) are garnering much attention in the Computational Science and Engineering…
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs)
Michael Penwarden, Shandian Zhe, Akil Narayan +1
Multifidelity simulation methodologies are often used in an attempt to judiciously combine low-fidelity and high-fidelity simulation results in an accuracy-increasing, cost-saving…