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Gaussian process inference of stochastic differential equations
Approximate Gaussian process inference for the drift function in stochastic differential equations Phys. Rev. E 98, 022109 (2018) - Approximate Bayes learning of stochastic differential equations (aps.org) Gaussian Process Approximations of Stochastic Differential Equations (mlr.press) Moment-Based Variational Inference for Stochastic Differential Equations (mlr.press) Variational Inference for Stochastic Differential Equations - Opper - 2019 - Annalen der Physik - Wiley Online Library Sparse Gaussian Processes for Stochastic Differential Equations | OpenReview
May 10, 2022Awesome Fluid Mechanics
A curated list of repositories related to fluid dynamics. Please send pull requests or raise issues to improve this list. Contents Educational Notebooks Lecture Series
May 7, 2022Shapash
🎉 What's new ? :warning: Starting from Shapash v2.0.0: '.compile()' parameters must be provided in the SmartExplainer init: :warning: For clearer initiation, method's parameters must be provided at the init: xpl = SmartExplainer(model, backend, preprocessing, postprocessing, features_groups) instead of xpl.compile(x, model, backend, preprocessing, postprocessing, features_groups) Version New Feature Description
May 7, 2022Projection Operators and Memory Function Formalism
Books Zwanzig-Nonequilibrium Statistical Mechanics Memory Functions, Projection Operators, and the Defect Technique (Lecture Notes in Physics) (Advances in Chemical Physics) - Memory Function Approahes to Stochastic Problems in Condensed Matter, Volume 62(1985). Boon, Yip - Molecular Hydrodynamics (Chapter 1, 2) Projection Operator Techniques in the theory of fluctuations by Bruce Berne in Modern Theoretical Chemistry. v.5 Statistical Mechanics, Part B Time-dependent processes Dieter Forster.-Hydrodynamic fluctuations, broken symmetry, and correlation functions Chapter 11 in Bruce J. Berne, Robert Pecora - Dynamic Light Scattering
May 7, 2022
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