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Found 5 papers, 0 papers with code
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.
