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09/27/2022
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter
Experimental data is costly to obtain, which makes it difficult to calib...
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06/23/2021
Machine learning structure preserving brackets for forecasting irreversible processes
Forecasting of time-series data requires imposition of inductive biases ...
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04/29/2021
Parallel implementation of a compatible high-order meshless method for the Stokes' equations
A parallel implementation of a compatible discretization scheme for stea...
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01/27/2021
Partition of unity networks: deep hp-approximation
Approximation theorists have established best-in-class optimal approxima...
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12/09/2020
Thermodynamically consistent physics-informed neural networks for hyperbolic systems
Physics-informed neural network architectures have emerged as a powerful...
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09/25/2020
A physics-informed operator regression framework for extracting data-driven continuum models
The application of deep learning toward discovery of data-driven models ...
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06/17/2020
A block coordinate descent optimizer for classification problems exploiting convexity
Second-order optimizers hold intriguing potential for deep learning, but...
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01/02/2020
Asymptotically compatible reproducing kernel collocation and meshfree integration for the peridynamic Navier equation
In this work, we study the reproducing kernel (RK) collocation method fo...
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12/10/2019