82 citations · 92 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU)
Jingye Tan, Govinda Anantha Padmanabha, Steven J. Yang +1
Recent progress in AI-enabled constitutive modeling has concentrated on moving from a purely data-driven paradigm to the enforcement of physical constraints and mechanistic princip…
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…
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…