49 citations · 94 across the 6 of their papers we have counts for
6 papers
Operator inference with roll outs for learning reduced models from scarce and low-quality data
Wayne Isaac Tan Uy, Dirk Hartmann, Benjamin Peherstorfer
Data-driven modeling has become a key building block in computational science and engineering. However, data that are available in science and engineering are typically scarce, oft…
Active-learning-based non-intrusive Model Order Reduction
Qinyu Zhuang, Dirk Hartmann, Hans Joachim Bungartz +1
The Model Order Reduction (MOR) technique can provide compact numerical models for fast simulation. Different from the intrusive MOR methods, the non-intrusive MOR does not require…
A Differentiable Solver Approach to Operator Inference
Dirk Hartmann, Lukas Failer
Model Order Reduction is a key technology for industrial applications in the context of digital twins. Key requirements are non-intrusiveness, physics-awareness, as well as robustn…
Model Order Reduction based on Runge-Kutta Neural Network
Qinyu Zhuang, Juan Manuel Lorenzi, Hans-Joachim Bungartz +1
Model Order Reduction (MOR) methods enable the generation of real-time-capable digital twins, which can enable various novel value streams in industry. While traditional projection…
Machine Learning-Based Optimal Mesh Generation in Computational Fluid Dynamics
Keefe Huang, Moritz Krügener, Alistair Brown +3
Computational Fluid Dynamics (CFD) is a major sub-field of engineering. Corresponding flow simulations are typically characterized by heavy computational resource requirements. Oft…
A neural network multigrid solver for the Navier-Stokes equations
Nils Margenberg, Dirk Hartmann, Christian Lessig +1
We present the deep neural network multigrid solver (DNN-MG) that we develop for the instationary Navier-Stokes equations. DNN-MG improves computational efficiency using a judiciou…