May 4, 2021 · Abstract:We study the learning of numerical algorithms for scientific computing, which combines mathematically driven, handcrafted design of ...
We show that we can learn high-order integrators for targeted families of differential equations without the need for computing integrator coefficients by hand.
Jul 9, 2022 · Abstract. We study the learning of numerical algorithms for scientific computing, which combines mathematically driven, handcrafted design ...
Personalized Algorithm Generation: A Case Study in Learning ODE Integrators ... 2017. We propose an algorithm for meta-learning that is model-agnostic, in the ...
... The relationship between a class of residual recurrent neural networks (RNN) and numerical integrators has been known since Rico-Martinez et al. (1992) ...
This work introduces Continuous-Time Meta-Learning (COMLN), a meta-learning algorithm where adaptation follows the dynamics of a gradient vector field, ...
May 4, 2021 · As a case study, we develop a machine learning approach that automatically learns effective solvers for initial value problems in the form of ...
We study the learning of numerical algorithms for scientific computing, which combines mathematically driven, handcrafted design of general algorithm ...
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We study the meta-learning of numerical algorithms for scientific computing, which combines the mathematically driven, handcrafted design of general ...
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Personalized Algorithm Generation: A Case Study in Learning ODE Integrators. Requirements. This project uses python 3.8 . Set up the project using the ...