May 5, 2024 · To resolve this problem, we propose a non-intrusive methodology with a novel gradient estimation technique to combine machine learning and ...
This paper formulates a general framework to describe these problems, and proposes a gradient-based algorithm to solve them in a unified way, ...
As an illustration of this approach, we study the adaptive generation of parameters for iterative solvers to accelerate the solution of differential equations.
In Section 2, we propose a general framework, called gradient-based meta-solving, to analyze and develop learning-based numerical methods. Using the frame-.
Jan 28, 2022 · In this paper, we tackle this issue by formulating a general framework to describe these problems, and propose a gradient-based algorithm to ...
Missing: Accelerating | Show results with:Accelerating
... numerical solvers by combining them with ML without any modification (Figure 1). Our proposed method expands the gradient-based meta-solving (GBMS) algorithm ...
This paper studies numerical solutions for parameterized partial differential equations (PDEs) with deep learning. Parametrized PDEs arise in many important ...
May 1, 2024 · Accelerating numerical methods by gradient-based meta-solving. S ... Principled Acceleration of Iterative Numerical Methods Using Machine Learning.
Jun 17, 2022 · In this paper, we propose a framework to analyze such learning-based acceleration approaches, where one can immediately identify a departure ...
Missing: solving. | Show results with:solving.
To resolve this problem, we propose a non-intrusive methodology with a novel gradient estimation technique to combine machine learning and legacy numerical ...