research
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12/02/2022
Operator inference with roll outs for learning reduced models from scarce and low-quality data
Data-driven modeling has become a key building block in computational sc...
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04/08/2022
Active-learning-based non-intrusive Model Order Reduction
The Model Order Reduction (MOR) technique can provide compact numerical ...
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07/05/2021
A Differentiable Solver Approach to Operator Inference
Model Order Reduction is a key technology for industrial applications in...
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03/25/2021
Model Order Reduction based on Runge-Kutta Neural Network
Model Order Reduction (MOR) methods enable the generation of real-time-c...
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02/25/2021
Machine Learning-Based Optimal Mesh Generation in Computational Fluid Dynamics
Computational Fluid Dynamics (CFD) is a major sub-field of engineering. ...
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08/26/2020
A neural network multigrid solver for the Navier-Stokes equations
We present a deep neural network multigrid solver (DNN-MG) that we devel...
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01/03/2020