Concepedia

A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime

Constantin Grigo, Phaedon‐Stelios Koutsourelakis

Journal of Computational Physics · 2019 · 41 citations · 75 references

Open access

Concepts

ComputingComputer SciencePhysicsComputational PhysicsMachine LearningScientific ComputingCoarse-grained ModelingDimensionality ReductionMarkov Chain Monte CarloMonte Carlo MethodsProbabilistic Machine LearningStatistical MechanicsBottom-up Coarse-grainingCoarse-grained SimulationCoarse-grainingHigh-dimensional Uncertainty QuantificationParameterization Of Coarse-grained ModelsSystematic Coarse-grainingTop-down Coarse-graining

References

75

ImageNet classification with deep convolutional neural networks

Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton · Communications of the ACM · 2017 · 75.7K citations · Full text

Computing, Computer Science, Computer Vision +12

Gradient-based learning applied to document recognition

Yann LeCun, Léon Bottou, Yoshua Bengio et al. · Proceedings of the IEEE · 1998 · 58.2K citations · Full text

Computing, Computer Science, Computer Vision +14

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Maziar Raissi, Paris Perdikaris, George Em Karniadakis · Journal of Computational Physics · 2018 · 16.9K citations · Full text

Natural Sciences, Computer Science, Physics +16

Auto-Encoding Variational Bayes

Diederik P. Kingma, Max Welling · UvA-DARE (University of Amsterdam) · 2013 · 15.6K citations · Full text

Computing, Computer Science, Machine Learning +16

The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations

Dongbin Xiu, George Em Karniadakis · SIAM Journal on Scientific Computing · 2002 · 4.7K citations

Natural Sciences, Computational Engineering, Mathematics +16