Zobrazeno 1 - 10
of 24 205
pro vyhledávání: '"BERNER, A."'
Autor:
Schreck, John, Sha, Yingkai, Chapman, William, Kimpara, Dhamma, Berner, Judith, McGinnis, Seth, Kazadi, Arnold, Sobhani, Negin, Kirk, Ben, Gagne II, David John
Recent advancements in artificial intelligence (AI) for numerical weather prediction (NWP) have significantly transformed atmospheric modeling. AI NWP models outperform traditional physics-based systems, such as the Integrated Forecast System (IFS),
Externí odkaz:
http://arxiv.org/abs/2411.07814
Cosmological neutral hydrogen (HI) surveys provide a promising tomographic probe of the post-reionization era and of the standard model of cosmology. Simulations of this signal are crucial for maximizing the utility of these surveys. We present a fas
Externí odkaz:
http://arxiv.org/abs/2410.01694
Autor:
Berner, Chad, Weber, Eric S.
Frames in a Hilbert space that are generated by operator orbits are vastly studied because of the applications in dynamic sampling and signal recovery. We demonstrate in this paper a representation theory for frames generated by operator orbits that
Externí odkaz:
http://arxiv.org/abs/2409.10706
Autor:
Shah, Freya, Patti, Taylor L., Berner, Julius, Tolooshams, Bahareh, Kossaifi, Jean, Anandkumar, Anima
Fourier Neural Operators (FNOs) excel on tasks using functional data, such as those originating from partial differential equations. Such characteristics render them an effective approach for simulating the time evolution of quantum wavefunctions, wh
Externí odkaz:
http://arxiv.org/abs/2409.03302
Autor:
Wang, Chuwei, Berner, Julius, Li, Zongyi, Zhou, Di, Wang, Jiayun, Bae, Jane, Anandkumar, Anima
Accurately predicting the long-term behavior of chaotic systems is crucial for various applications such as climate modeling. However, achieving such predictions typically requires iterative computations over a dense spatiotemporal grid to account fo
Externí odkaz:
http://arxiv.org/abs/2408.05177
Autor:
Balzer, Juan, Berner, Rico, Lüdge, Kathy, Wieczorek, Sebastian, Kurths, Jürgen, Yanchuk, Serhiy
Canard cascading (CC) is observed in dynamical networks with global adaptive coupling. It is a fast-slow phenomenon characterized by a recurrent sequence of fast transitions between distinct and slowly evolving quasi-stationary states. In this letter
Externí odkaz:
http://arxiv.org/abs/2407.20758
Autor:
Berner, Lukas, Meyerhenke, Henning
Assessing and improving the robustness of a graph $G$ are critical steps in network design and analysis. To this end, we consider the optimisation problem of removing $k$ edges from $G$ such that the resulting graph has minimal robustness, simulating
Externí odkaz:
http://arxiv.org/abs/2407.11521
Autor:
Sun, Jingtong, Berner, Julius, Richter, Lorenz, Zeinhofer, Marius, Müller, Johannes, Azizzadenesheli, Kamyar, Anandkumar, Anima
The task of sampling from a probability density can be approached as transporting a tractable density function to the target, known as dynamical measure transport. In this work, we tackle it through a principled unified framework using deterministic
Externí odkaz:
http://arxiv.org/abs/2407.07873
We propose accelerating the simulation of Lagrangian dynamics, such as fluid flows, granular flows, and elastoplasticity, with neural-operator-based reduced-order modeling. While full-order approaches simulate the physics of every particle within the
Externí odkaz:
http://arxiv.org/abs/2407.03925
Diffusion models have recently achieved success in solving Bayesian inverse problems with learned data priors. Current methods build on top of the diffusion sampling process, where each denoising step makes small modifications to samples from the pre
Externí odkaz:
http://arxiv.org/abs/2407.01521