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05/24/2023
Reversible and irreversible bracket-based dynamics for deep graph neural networks
Recent works have shown that physics-inspired architectures allow the tr...
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11/22/2022
Time Series Forecasting with Hypernetworks Generating Parameters in Advance
Forecasting future outcomes from recent time series data is not easy, es...
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10/10/2022
Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting
Continuous-time dynamics models, such as neural ordinary differential eq...
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10/01/2022
Parameter-varying neural ordinary differential equations with partition-of-unity networks
In this study, we propose parameter-varying neural ordinary differential...
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07/13/2022
AdamNODEs: When Neural ODE Meets Adaptive Moment Estimation
Recent work by Xia et al. leveraged the continuous-limit of the classica...
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02/25/2022
Stochastic Galerkin methods for linear stability analysis of systems with parametric uncertainty
We present a method for linear stability analysis of systems with parame...
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02/07/2022
Unsupervised physics-informed disentanglement of multimodal data for high-throughput scientific discovery
We introduce physics-informed multimodal autoencoders (PIMA) - a variati...
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11/11/2021
Climate Modeling with Neural Diffusion Equations
Owing to the remarkable development of deep learning technology, there h...
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09/11/2021
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling
Discovery of dynamical systems from data forms the foundation for data-d...
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07/07/2021
Probabilistic partition of unity networks: clustering based deep approximation
Partition of unity networks (POU-Nets) have been shown capable of realiz...
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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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02/28/2021
On Surrogate Learning for Linear Stability Assessment of Navier-Stokes Equations with Stochastic Viscosity
We study linear stability of solutions to the Navier–Stokes equations wi...
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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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12/04/2020
DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation
We present a method for learning dynamics of complex physical processes ...
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10/28/2020
Parameterized Neural Ordinary Differential Equations: Applications to Computational Physics Problems
This work proposes an extension of neural ordinary differential equation...
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06/15/2020
Alternating Energy Minimization Methods for Multi-term Matrix Equations
We develop computational methods for approximating the solution of a lin...
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06/11/2019
Solving Large-Scale 0-1 Knapsack Problems and its Application to Point Cloud Resampling
0-1 knapsack is of fundamental importance in computer science, business,...
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12/20/2018