statmech

Physics-informed neural networks 2019-10-15 – 2022-12-09

Machine learning for physical sciences Turbulent mixing at the boundary between two disciplines with differing inertia and viscosity 2017-05-15 – 2022-12-07

Mind as statistical learner 2020-06-23 – 2022-12-06

Deep learning as a dynamical system 2018-08-13 – 2022-10-30

The edge of chaos Computation, evolution, competition and other past-times of faculty 2016-12-01 – 2022-10-30

Φ-Flow A modern python computational fluid dynamics library for ML research 2022-06-24 – 2022-10-04

Ensemble Kalman methods for training neural networks Data assimilation for network weights 2022-09-20

Informations Entropies and other measures of surprise 2011-11-25 – 2022-08-30

Hydrology, applied Rivers, aquifers and other wet things that can flood your house 2020-09-07 – 2022-08-30

Neural PDE operator learning 2019-10-15 – 2022-08-29

Machine learning for partial differential equations 2017-05-15 – 2022-08-29

Neural nets with implicit layers Also, declarative networks, bi-level optimization and other ingenious uses of the implicit function theorem 2020-12-08 – 2022-08-09

Scaling laws for very large neural nets Compute/size/data tradeoffs 2021-01-14 – 2022-08-09

Differentiable PDE solvers 2017-05-15 – 2022-07-20

Fun tricks in non-convex optimisation 2014-10-04 – 2022-07-14

Learnable coarse-graining Approximate meso-scale physics 2020-08-24 – 2022-07-13

Multi fidelity models Data-driven multi-scale sampling 2020-08-24 – 2022-07-12

Hydrology, applied Rivers, aquifers and other wet things that can flood your house 2020-09-07 – 2022-06-30

Overparameterization in large models Improper learning, benign overfitting, double descent 2018-04-04 – 2022-05-27

Emergent spacetime What are qubits again? 2022-05-26

Gradient descent First order of business 2014-10-04 – 2022-04-28

This is a simulation Can the automaton learn to play the game of life? 2017-04-04 – 2022-04-09

Predictive coding Does the model that our brains do bayesian variational prediction make any actual predictions about our brains? 2011-11-27 – 2022-04-04

Reservoir Computing 2022-03-28

Learning with conservation laws, invariances and symmetries 2020-04-11 – 2022-02-25

Politics as statistical learner 2022-01-31 – 2022-02-17

Ergodicity and mixing Things that probably happen eventually on average 2011-10-17 – 2022-02-13

Neural nets for “implicit representations” 2021-01-21 – 2022-02-01

Neural nets with basis decomposition layers 2021-03-09 – 2022-02-01

Running neural nets backwards 2022-01-29

Quantum computing 2017-12-14 – 2022-01-21

Mind as statistical learner 2022-01-09

Lévy processes 2017-05-29 – 2021-11-17

Neural nets for “implicit representations” 2021-01-21 – 2021-11-16

Optimisation 2014-10-04 – 2021-11-11

Random neural networks 2017-02-17 – 2021-10-12

Gradient descent at scale Practical implementation of large optimisations 2021-07-14 – 2021-09-28

Algorithmic statistics Probably also algorithmic information theory 2014-07-25 – 2021-09-26

Meta learning Few-shot learning, learning fast weights, learning to learn 2021-09-16

Sequential experiments Especially multiple sequential experiments 2021-08-04

Morphogenesis 2011-01-19 – 2021-07-26

Point process intensities and statistical estimation thereof 2016-08-01 – 2021-07-07

Energy based models Inference with kinda-tractable un-normalized densities 2021-06-07

Decoupling the economy from energy 2021-05-21

Power generation, engineering and use Miscellaneous links on the magical terrible process of keeping the lights on 2021-05-04

Path integral formulations Feynman path integrals, esp for stochastic processes 2021-04-19

Differentiable model selection Differentiable hyperparameter search, and architecture search, and optimisation optimisation by optimisation and so on 2020-09-25 – 2021-04-13

Prediction processes Some kind of weird time series formalism 2021-04-09

Statistical mechanics of statistics 2016-12-01 – 2021-01-06

Why does deep learning work? Are we in the pocket of Big VRAM? 2017-05-30 – 2020-12-14

Observability and sensitivity in learning dynamical systems Parameter identifiability in dynamical models 2020-11-09

Time Certain quirks of entropy 2020-10-20

Automatic design of experiments Minesweeper++ 2017-04-11 – 2020-10-13

AutoML 2017-07-17 – 2020-10-02

Emulators and surrogate models Shortcuts in scientific simulation using ML 2020-08-12 – 2020-08-26

Bushfire models 2020-09-07 – 2020-08-19

Empirical estimation of information Informing yourself from your data how informative your data was 2011-04-19 – 2020-04-28

Potential theory in probability Something about harmonic functions or whatever 2020-02-12

Spatial point process and their statistics 2016-08-17 – 2019-12-04

Gradient descent, Higher order 2019-10-26

Semidefinite proramming 2019-06-29

Point processes 2016-08-01 – 2019-02-18

Pattern formation 2014-12-23 – 2018-10-09

Optimisation, combinatorial 2018-08-11

Quantum information in physics What are qubits again? 2017-07-24 – 2017-12-14

Gradient descent, continuous, primal/dual formulations. 2017-08-07

Thermodynamics of life 2014-09-23 – 2017-05-30

Distributed statistica inference 2016-10-11

Statistical learning theory Eventually including structural risk minimisation, risk bounds, hopefully-uniform convergence rates, VC-dimension, generalisation-and-stability framings etc 2016-07-06 – 2016-08-16

Material basis of the economy 2011-10-17 – 2016-08-01

Dynamical systems 2016-04-26 – 2016-07-27

Thermodynamics 2015-02-20 – 2016-01-18

Coarse graining 2014-11-11 – 2015-12-02

Statistical mechanics 2015-11-14

Earthquakes 2015-07-14

Complexity 2011-11-25 – 2015-04-11

Computational mechanics 2011-10-17 – 2015-01-02

Flocking 2011-04-19 – 2014-11-20

Econophysics 2011-04-13

Simulation for the social sciences 2010-09-28