118 citations · 334 across the 18 of their papers we have counts for
19 papers
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)…
Batch Multi-Fidelity Active Learning with Budget Constraints
Shibo Li, Jeff M. Phillips, Xin Yu +2
Learning functions with high-dimensional outputs is critical in many applications, such as physical simulation and engineering design. However, collecting training examples for the…
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…
Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization
Tan Nguyen, Richard G. Baraniuk, Robert M. Kirby +2
Transformers have achieved remarkable success in sequence modeling and beyond but suffer from quadratic computational and memory complexities with respect to the length of the inpu…
Adaptive Self-supervision Algorithms for Physics-informed Neural Networks
Shashank Subramanian, Robert M. Kirby, Michael W. Mahoney +1
Physics-informed neural networks (PINNs) incorporate physical knowledge from the problem domain as a soft constraint on the loss function, but recent work has shown that this can l…
Infinite-Fidelity Coregionalization for Physical Simulation
Shibo Li, Zheng Wang, Robert M. Kirby +1
Multi-fidelity modeling and learning are important in physical simulation-related applications. It can leverage both low-fidelity and high-fidelity examples for training so as to r…
Variational Inference for Nonlinear Inverse Problems via Neural Net Kernels: Comparison to Bayesian Neural Networks, Application to Topology Optimization
Vahid Keshavarzzadeh, Robert M. Kirby, Akil Narayan
Inverse problems and, in particular, inferring unknown or latent parameters from data are ubiquitous in engineering simulations. A predominant viewpoint in identifying unknown para…
ENO-Based High-Order Data-Bounded and Constrained Positivity-Preserving Interpolation
Timbwaoga A. J. Ouermi, Robert M. Kirby, Martin Berzins
A number of key scientific computing applications that are based upon tensor-product grid constructions, such as numerical weather prediction (NWP) and combustion simulations, requ…
Weight Matrix Dimensionality Reduction in Deep Learning via Kronecker Multi-layer Architectures
Jarom D. Hogue, Robert M. Kirby, Akil Narayan
Deep learning using neural networks is an effective technique for generating models of complex data. However, training such models can be expensive when networks have large model c…
Convex Optimization-Based Structure-Preserving Filter For Multidimensional Finite Element Simulations
Vidhi Zala, Akil Narayan, Robert M Kirby
In simulation sciences, it is desirable to capture the real-world problem features as accurately as possible. Methods popular for scientific simulations such as the finite element…
Machine Learning in Heterogeneous Porous Materials
Marta D'Elia, Hang Deng, Cedric Fraces +21
The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material scienc…
AutoIP: A United Framework to Integrate Physics into Gaussian Processes
Da Long, Zheng Wang, Aditi Krishnapriyan +3
Physical modeling is critical for many modern science and engineering applications. From a data science or machine learning perspective, where more domain-agnostic, data-driven mod…
GP-HMAT: Scalable, Gaussian Process Regression with Hierarchical Low-Rank Matrices
Vahid Keshavarzzadeh, Shandian Zhe, Robert M. Kirby +1
A Gaussian process (GP) is a powerful and widely used regression technique. The main building block of a GP regression is the covariance kernel, which characterizes the relationshi…
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…
Characterizing possible failure modes in physics-informed neural networks
Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe +2
Recent work in scientific machine learning has developed so-called physics-informed neural network (PINN) models. The typical approach is to incorporate physical domain knowledge a…
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…
Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks
Shibo Li, Robert M. Kirby, Shandian Zhe
Bayesian optimization (BO) is a powerful approach for optimizing black-box, expensive-to-evaluate functions. To enable a flexible trade-off between the cost and accuracy, many appl…