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Found 9 papers, 0 papers with code
Uncertainty quantification of neural network models of evolving processes via Langevin sampling
We propose a scalable, approximate inference hypernetwork framework for a general model of history-dependent processes.
Input Specific Neural Networks
The black-box nature of neural networks limits the ability to encode or impose specific structural relationships between inputs and outputs.
Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks
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 network.
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models
Specifically, $L_0$+SVGD demonstrates superior resilience to noise, the ability to perform well in extrapolated regions, and a faster convergence rate to an optimal solution.
Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes
In this work we present comparisons of the parametric uncertainty quantification of neural networks modeling complex spatial-temporal processes with Hamiltonian Monte Carlo and Stein variational gradient descent and its projected variant.
Accurate Data-Driven Surrogates of Dynamical Systems for Forward Propagation of Uncertainty
Stochastic collocation (SC) is a well-known non-intrusive method of constructing surrogate models for uncertainty quantification.
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics
Data-driven constitutive modeling with neural networks has received increased interest in recent years due to its ability to easily incorporate physical and mechanistic constraints and to overcome the challenging and time-consuming task of formulating phenomenological constitutive laws that can accurately capture the observed material response.
Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen
Data-driven constitutive modeling frameworks based on neural networks and classical representation theorems have recently gained considerable attention due to their ability to easily incorporate constitutive constraints and their excellent generalization performance.
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter
This new data leads to a Bayesian update of the parameters by the KF, which is used to enhance the state representation.
