118 citations · 334 across the 18 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
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…