Zobrazeno 1 - 10
of 133
pro vyhledávání: '"SCHAEFFER, HAYDEN"'
This work concerns the zeroth-order global minimization of continuous nonconvex functions with a unique global minimizer and possibly multiple local minimizers. We formulate a theoretical framework for inexact proximal point (IPP) methods for global
Externí odkaz:
http://arxiv.org/abs/2412.11485
In-Context Operator Networks (ICONs) are models that learn operators across different types of PDEs using a few-shot, in-context approach. Although they show successful generalization to various PDEs, existing methods treat each data point as a singl
Externí odkaz:
http://arxiv.org/abs/2411.16063
We propose a novel fine-tuning method to achieve multi-operator learning through training a distributed neural operator with diverse function data and then zero-shot fine-tuning the neural network using physics-informed losses for downstream tasks. O
Externí odkaz:
http://arxiv.org/abs/2411.07239
Neural scaling laws play a pivotal role in the performance of deep neural networks and have been observed in a wide range of tasks. However, a complete theoretical framework for understanding these scaling laws remains underdeveloped. In this paper,
Externí odkaz:
http://arxiv.org/abs/2410.00357
Symbolic encoding has been used in multi-operator learning as a way to embed additional information for distinct time-series data. For spatiotemporal systems described by time-dependent partial differential equations, the equation itself provides an
Externí odkaz:
http://arxiv.org/abs/2409.11609
We propose PROSE-FD, a zero-shot multimodal PDE foundational model for simultaneous prediction of heterogeneous two-dimensional physical systems related to distinct fluid dynamics settings. These systems include shallow water equations and the Navier
Externí odkaz:
http://arxiv.org/abs/2409.09811
Single-operator learning involves training a deep neural network to learn a specific operator, whereas recent work in multi-operator learning uses an operator embedding structure to train a single neural network on data from multiple operators. Thus,
Externí odkaz:
http://arxiv.org/abs/2408.16168
Foundation models, such as large language models, have demonstrated success in addressing various language and image processing tasks. In this work, we introduce a multi-modal foundation model for scientific problems, named PROSE-PDE. Our model, desi
Externí odkaz:
http://arxiv.org/abs/2404.12355
Neural operators have been applied in various scientific fields, such as solving parametric partial differential equations, dynamical systems with control, and inverse problems. However, challenges arise when dealing with input functions that exhibit
Externí odkaz:
http://arxiv.org/abs/2310.18888
Approximating nonlinear differential equations using a neural network provides a robust and efficient tool for various scientific computing tasks, including real-time predictions, inverse problems, optimal controls, and surrogate modeling. Previous w
Externí odkaz:
http://arxiv.org/abs/2309.16816