4 days ago · Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are first-principled, explainable, ...

Jun 10, 2024 · Deep learning via dynamical systems: An approximation perspective. arXiv ... Solving flows of dynamical systems by deep neural networks and a novel deep learning ...

6 days ago · A novel deep learning model (i.e., SKCAE) integrating physics and data is proposed to construct the nonlinear observation functions in Koopman models.

Jun 7, 2024 · Approximation of dynamical systems by continuous time recurrent neural networks. ... A diffusion theory for deep learning dynamics: Stochastic gradient ...

7 days ago · The remainder of this paper is structured as follows: In section 2, we discuss residual neural networks inspired by dynamical systems by introducing the ...

Jun 19, 2024 · Lecture 1: introduction, deep learning as optimal control, dynamical systems and deep neural networks. Equivariant neural networks. • Lecture 2: Adversarial ...

Jun 16, 2024 · Solving partial differential equations (PDEs) efficiently is essential for analyzing complex physical systems. Recent advancements in leveraging deep learning ...

Jun 5, 2024 · The system training/self-organizing AE starts by discussing the mystery of synaptic changes induced by local chemical changes that can be coordinated ...

Duration: 25:58
Posted: 7 days ago

Jun 10, 2024 · This paper describes a method for learning low-dimensional approximations of nonlinear dynamical systems, based on neural network approximations of the ...