Wave Physics as an Analog Recurrent Neural Network
Hughes et al.: https://arxiv.org/abs/1904.12831
#Physics #Artificialintelligence #DeepLearning
@ArtificialIntelligenceArticles
Hughes et al.: https://arxiv.org/abs/1904.12831
#Physics #Artificialintelligence #DeepLearning
@ArtificialIntelligenceArticles
arXiv.org
Wave Physics as an Analog Recurrent Neural Network
Analog machine learning hardware platforms promise to be faster and more energy-efficient than their digital counterparts. Wave physics, as found in acoustics and optics, is a natural candidate...
Machine Learning for Physics and the Physics of Learning Tutorials"
Videos and slides, by IPAM (an NSF Math Institute at UCLA dedicated to promoting the interaction of math with other disciplines):
https://www.ipam.ucla.edu/programs/workshops/machine-learning-for-physics-and-the-physics-of-learning-tutorials/?tab=schedule
https://t.iss.one/ArtificialIntelligenceArticles
#MLP2019 #MachineLearning #Physics
Videos and slides, by IPAM (an NSF Math Institute at UCLA dedicated to promoting the interaction of math with other disciplines):
https://www.ipam.ucla.edu/programs/workshops/machine-learning-for-physics-and-the-physics-of-learning-tutorials/?tab=schedule
https://t.iss.one/ArtificialIntelligenceArticles
#MLP2019 #MachineLearning #Physics
Learning Symbolic Physics with Graph Networks
Cranmer et al.: https://arxiv.org/abs/1909.05862
#MachineLearning #GraphNetworks #Physics
Cranmer et al.: https://arxiv.org/abs/1909.05862
#MachineLearning #GraphNetworks #Physics
Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model
David John Gagne II, Hannah M. Christensen, Aneesh C. Subramanian, Adam H. Monahan : https://arxiv.org/abs/1909.04711
#GenerativeAdversarialNetworks #MachineLearning #Physics
David John Gagne II, Hannah M. Christensen, Aneesh C. Subramanian, Adam H. Monahan : https://arxiv.org/abs/1909.04711
#GenerativeAdversarialNetworks #MachineLearning #Physics
arXiv.org
Machine Learning for Stochastic Parameterization: Generative...
Stochastic parameterizations account for uncertainty in the representation of
unresolved sub-grid processes by sampling from the distribution of possible
sub-grid forcings. Some existing...
unresolved sub-grid processes by sampling from the distribution of possible
sub-grid forcings. Some existing...
DyANE: Dynamics-aware node embedding for temporal networks
Koya Sato, Mizuki Oka, Alain Barrat, Ciro Cattuto : https://arxiv.org/abs/1909.05976
#Physics #Society #MachineLearning #SocialNetworks
Koya Sato, Mizuki Oka, Alain Barrat, Ciro Cattuto : https://arxiv.org/abs/1909.05976
#Physics #Society #MachineLearning #SocialNetworks
Artificial intelligence probes dark matter in the universe
By Oliver Morsch : https://ethz.ch/en/news-and-events/eth-news/news/2019/09/artificial-intelligence-probes-dark-matter-in-the-universe.html
#ArtificialIntelligence #DeepLearning #Physics
By Oliver Morsch : https://ethz.ch/en/news-and-events/eth-news/news/2019/09/artificial-intelligence-probes-dark-matter-in-the-universe.html
#ArtificialIntelligence #DeepLearning #Physics
ethz.ch
Artificial intelligence probes dark matter in the universe
Hamiltonian Graph Networks with ODE Integrators
Sanchez-Gonzalez et al.: https://arxiv.org/abs/1909.12790
#Hamiltonian #MachineLearning #Physics
Sanchez-Gonzalez et al.: https://arxiv.org/abs/1909.12790
#Hamiltonian #MachineLearning #Physics
Fermionic neural-network states for ab-initio electronic structure
Choo et al.: https://arxiv.org/abs/1909.12852
#Physics #MachineLearning #NeuralNetworks
Choo et al.: https://arxiv.org/abs/1909.12852
#Physics #MachineLearning #NeuralNetworks
Learning Symbolic Physics with Graph Networks
Miles D. Cranmer, Rui Xu, Peter Battaglia, Shirley Ho : https://arxiv.org/abs/1909.05862
#GraphNetworks #MachineLearning #Physics
Miles D. Cranmer, Rui Xu, Peter Battaglia, Shirley Ho : https://arxiv.org/abs/1909.05862
#GraphNetworks #MachineLearning #Physics
Learning Symbolic Physics with Graph Networks
Miles D. Cranmer, Rui Xu, Peter Battaglia, Shirley Ho : https://arxiv.org/abs/1909.05862
#GraphNetworks #MachineLearning #Physics
Miles D. Cranmer, Rui Xu, Peter Battaglia, Shirley Ho : https://arxiv.org/abs/1909.05862
#GraphNetworks #MachineLearning #Physics
SO(8) Supergravity and the Magic of Machine Learning
Comsa et al.: https://arxiv.org/abs/1906.00207
#ArtificialIntelligence #DeepLearning #Physics
Comsa et al.: https://arxiv.org/abs/1906.00207
#ArtificialIntelligence #DeepLearning #Physics
arXiv.org
SO(8) Supergravity and the Magic of Machine Learning
Using de Wit-Nicolai $D=4\;\mathcal{N}=8\;SO(8)$ supergravity as an example, we show how modern Machine Learning software libraries such as Google's TensorFlow can be employed to greatly simplify...
Learning Symbolic Physics with Graph Networks
Miles D. Cranmer, Rui Xu, Peter Battaglia, Shirley Ho : https://arxiv.org/abs/1909.05862 #GraphNetworks
#MachineLearning #Physics
Miles D. Cranmer, Rui Xu, Peter Battaglia, Shirley Ho : https://arxiv.org/abs/1909.05862 #GraphNetworks
#MachineLearning #Physics
Machine learning in physics: The pitfalls of poisoned training sets
Fang et al.: https://arxiv.org/abs/2003.05087
https://t.iss.one/ArtificialIntelligenceArticles
#MachineLearning #NeuralNetworks #Physics
Fang et al.: https://arxiv.org/abs/2003.05087
https://t.iss.one/ArtificialIntelligenceArticles
#MachineLearning #NeuralNetworks #Physics
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Physics-based Deep Learning
Thuerey et al.: https://arxiv.org/abs/2109.05237
#MachineLearning #DeepLearning #Physics
Thuerey et al.: https://arxiv.org/abs/2109.05237
#MachineLearning #DeepLearning #Physics