3 citations · 6 across the 7 of their papers we have counts for
8 papers
Interval and fuzzy physics-augmented neural networks (iPANN and fPANN) for uncertainty quantification and propagation in constitutive modeling
Somesh Pratap Singh, Govinda Anantha Padmanabha, Jingye Tan +4
Constitutive modeling under uncertainty remains a central challenge for reliable mechanics simulations, particularly when the available stress-deformation data are sparse, noisy, o…
GenUQ: Predictive Uncertainty Estimates via Generative Hyper-Networks
Tian Yu Yen, Reese E. Jones, Ravi G. Patel
Operator learning is a recently developed generalization of regression to mappings between functions. It promises to drastically reduce expensive numerical integration of PDEs to f…
Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials
Steven Yang, Michal Levin, Govinda Anantha Padmanabha +6
Multi-material 3D printing, particularly through polymer jetting, enables the fabrication of digital materials by mixing distinct photopolymers at the micron scale within a single…
Uncertainty quantification of neural network models of evolving processes via Langevin sampling
Cosmin Safta, Reese E. Jones, Ravi G. Patel +4
We propose a scalable, approximate inference hypernetwork framework for a general model of history-dependent processes. The flexible data model is based on a neural ordinary differ…
Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks
Govinda Anantha Padmanabha, Cosmin Safta, Nikolaos Bouklas +1
We propose a Stein variational gradient descent method to concurrently sparsify, train, and provide uncertainty quantification of a complexly parameterized model such as a neural n…
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models
Govinda Anantha Padmanabha, Jan Niklas Fuhg, Cosmin Safta +2
Most scientific machine learning (SciML) applications of neural networks involve hundreds to thousands of parameters, and hence, uncertainty quantification for such models is plagu…
Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response
Ravi Patel, Cosmin Safta, Reese E. Jones
Composite materials with different microstructural material symmetries are common in engineering applications where grain structure, alloying and particle/fiber packing are optimiz…
Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes
Jeremiah Hauth, Cosmin Safta, Xun Huan +2
The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural network models have proved to be adept at mode…