no code implementations • 13 May 2024 • Nga T. T. Nguyen-Fotiadis, Robert Chiodi, Michael McKerns, Daniel Livescu, Andrew Sornborger
In particular, we show that our probabilistic flux limiter outperforms standard limiters, and can be successively improved upon (up to a point) by expanding the set of probabilistically chosen flux limiting functions.
no code implementations • 1 Dec 2022 • Dima Tretiak, Arvind T. Mohan, Daniel Livescu
Generative Adversarial Networks (GANs) have received wide acclaim among the machine learning (ML) community for their ability to generate realistic 2D images.
no code implementations • 20 Sep 2022 • Satish Karra, Mohamed Mehana, Nicholas Lubbers, Yu Chen, Abdourahmane Diaw, Javier E. Santos, Aleksandra Pachalieva, Robert S. Pavel, Jeffrey R. Haack, Michael McKerns, Christoph Junghans, Qinjun Kang, Daniel Livescu, Timothy C. Germann, Hari S. Viswanathan
Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements.
no code implementations • 10 May 2022 • Yen Ting Lin, Yifeng Tian, Danny Perez, Daniel Livescu
We propose to adopt statistical regression as the projection operator to enable data-driven learning of the operators in the Mori--Zwanzig formalism.
1 code implementation • 25 Oct 2021 • Michael Woodward, Yifeng Tian, Criston Hyett, Chris Fryer, Daniel Livescu, Mikhail Stepanov, Michael Chertkov
Within this hierarchy, two new parameterized smoothing kernels are developed in order to increase the flexibility of the learn-able SPH simulators.
BIG-bench Machine Learning Physics-informed machine learning
1 code implementation • 21 Jun 2021 • Bryan E. Kaiser, Juan A. Saenz, Maike Sonnewald, Daniel Livescu
The advent of big data has vast potential for discovery in natural phenomena ranging from climate science to medicine, but overwhelming complexity stymies insight.
no code implementations • ICLR Workshop DeepDiffEq 2019 • Arvind T. Mohan, Daniel Livescu, Michael Chertkov
One of the fundamental driving phenomena for applications in engineering, earth sciences and climate is fluid turbulence.
no code implementations • ICLR Workshop DeepDiffEq 2019 • Arvind T. Mohan, Nicholas Lubbers, Daniel Livescu, Michael Chertkov
Deep learning approaches have shown much promise for physical sciences, especially in dimensionality reduction and compression of large datasets.
no code implementations • 31 Jan 2020 • Arvind T. Mohan, Nicholas Lubbers, Daniel Livescu, Michael Chertkov
In the recent years, deep learning approaches have shown much promise in modeling complex systems in the physical sciences.
Computational Physics
no code implementations • 20 May 2019 • Balasubramanya T. Nadiga, Chiyu Jiang, Daniel Livescu
We focus on improving the accuracy of an approximate model of a multiscale dynamical system that uses a set of parameter-dependent terms to account for the effects of unresolved or neglected dynamics on resolved scales.
no code implementations • 28 Feb 2019 • Arvind Mohan, Don Daniel, Michael Chertkov, Daniel Livescu
High-fidelity modeling of turbulent flows is one of the major challenges in computational physics, with diverse applications in engineering, earth sciences and astrophysics, among many others.
Fluid Dynamics Chaotic Dynamics Computational Physics