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Found 6 papers, 1 papers with code
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.
Machine learning structure preserving brackets for forecasting irreversible processes
Forecasting of time-series data requires imposition of inductive biases to obtain predictive extrapolation, and recent works have imposed Hamiltonian/Lagrangian form to preserve structure for systems with reversible dynamics.
Partition of unity networks: deep hp-approximation
Approximation theorists have established best-in-class optimal approximation rates of deep neural networks by utilizing their ability to simultaneously emulate partitions of unity and monomials.
A physics-informed operator regression framework for extracting data-driven continuum models
The application of deep learning toward discovery of data-driven models requires careful application of inductive biases to obtain a description of physics which is both accurate and robust.
A block coordinate descent optimizer for classification problems exploiting convexity
By alternating between a second-order method to find globally optimal parameters for the linear layer and gradient descent to train the hidden layers, we ensure an optimal fit of the adaptive basis to data throughout training.
Robust Training and Initialization of Deep Neural Networks: An Adaptive Basis Viewpoint
Motivated by the gap between theoretical optimal approximation rates of deep neural networks (DNNs) and the accuracy realized in practice, we seek to improve the training of DNNs.
