Zobrazeno 1 - 10
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pro vyhledávání: '"P. Ober"'
Hf and Zr nitrides are promising compounds for many technologically important areas, including high temperature structural applications, quantum computing and solar/optical applications. This article reports on a comprehensive first-principles statis
Externí odkaz:
http://arxiv.org/abs/2410.14013
The depth of networks plays a crucial role in the effectiveness of deep learning. However, the memory requirement for backpropagation scales linearly with the number of layers, which leads to memory bottlenecks during training. Moreover, deep network
Externí odkaz:
http://arxiv.org/abs/2410.09537
Differential equations posed on quadratic matrix Lie groups arise in the context of classical mechanics and quantum dynamical systems. Lie group numerical integrators preserve the constants of motions defining the Lie group. Thus, they respect import
Externí odkaz:
http://arxiv.org/abs/2408.13043
Motivated by mechanical systems with symmetries, we focus on optimal control problems possessing symmetries. Following recent works, which generalized the classical concept of static turnpike to manifold turnpike, we extend the exponential turnpike p
Externí odkaz:
http://arxiv.org/abs/2406.14286
The simulation of systems that act on multiple time scales is challenging. A stable integration of the fast dynamics requires a highly accurate approximation whereas for the simulation of the slow part, a coarser approximation is accurate enough. Wit
Externí odkaz:
http://arxiv.org/abs/2406.12991
The primary objective of most lead optimization campaigns is to enhance the binding affinity of ligands. For large molecules such as antibodies, identifying mutations that enhance antibody affinity is particularly challenging due to the combinatorial
Externí odkaz:
http://arxiv.org/abs/2406.07263
Autor:
Ober-Reynolds, Daniel
Missing data is pervasive in econometric applications, and rarely is it plausible that the data are missing (completely) at random. This paper proposes a methodology for studying the robustness of results drawn from incomplete datasets. Selection is
Externí odkaz:
http://arxiv.org/abs/2406.06804
Autor:
Maslovskaya, Sofya, Ober-Blöbaum, Sina
Deep learning is widely used in tasks including image recognition and generation, in learning dynamical systems from data and many more. It is important to construct learning architectures with theoretical guarantees to permit safety in the applicati
Externí odkaz:
http://arxiv.org/abs/2406.04104
Fractional dissipation is a powerful tool to study non-local physical phenomena such as damping models. The design of geometric, in particular, variational integrators for the numerical simulation of such systems relies on a variational formulation o
Externí odkaz:
http://arxiv.org/abs/2403.18362
Autor:
Ober, Sebastian W., Artemev, Artem, Wagenländer, Marcel, Grobins, Rudolfs, van der Wilk, Mark
Gaussian processes (GPs) are a mature and widely-used component of the ML toolbox. One of their desirable qualities is automatic hyperparameter selection, which allows for training without user intervention. However, in many realistic settings, appro
Externí odkaz:
http://arxiv.org/abs/2402.09849