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Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics | Scientific Reports
Nature
Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated...
3 months ago
Physics-Informed Neural Networks: An Application-Centric Guide
Towards Data Science
When it comes to applying machine learning to physical system modeling, it is more and more common to see practitioners moving away from a...
4 months ago
On fast simulation of dynamical system with neural vector enhanced numerical solver | Scientific Reports
Nature
The large-scale simulation of dynamical systems is critical in numerous scientific and engineering disciplines.
9 months ago
Deep learning from a dynamical viewpoint
Phys.org
NUS mathematicians have developed a new theoretical framework based on dynamical systems to understand when and how a deep neural network...
21 months ago
2022 & beyond: Algorithms for efficient deep learning
Google Research
Posted by Sanjiv Kumar, VP and Google Fellow, Google Research (This is Part 4 in our series of posts covering different topical areas of...
16 months ago
Deep neural operator-driven real-time inference to enable digital twin solutions for nuclear energy systems | Scientific ...
Nature
This paper focuses on the feasibility of deep neural operator network (DeepONet) as a robust surrogate modeling method within the context of...
5 months ago
Machine learning for parameter estimation
PNAS
Mathematical modeling provides the critical infrastructure for the quantitative analysis of any discipline.
15 months ago
Discovering Differential Equations with Physics-Informed Neural Networks and Symbolic Regression
Towards Data Science
Differential equations serve as a powerful framework to capture and understand the dynamic behaviors of physical systems. By describing how...
11 months ago
Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows | Scientific Reports
Nature
This work proposes a new machine learning (ML)-based paradigm aiming to enhance the computational efficiency of non-equilibrium reacting...
9 months ago
Neural ODEs: breakdown of another deep learning breakthrough
Towards Data Science
Hi everyone! If you're reading this article, most probably you're catching up with the recent advances that happen in the AI world.
60 months ago