# statmech

Multi fidelity models
Data-driven multi-scale sampling, multi-resolution, super-resolution
2020-08-24
– 2023-11-20Machine learning for partial differential equations
2017-05-15
– 2023-11-17Feedback system identification, not necessarily linear
Learning dynamics from data
2016-08-01
– 2023-11-15Hydrology, applied
Rivers, aquifers and other wet things that can flood your house
2020-09-07
– 2023-10-27Physics-informed neural networks
2019-10-15
– 2023-10-19Neural PDE operator learning
Especially forward operators. Image-to-image regression, where the images encode a physical process.
2019-10-15
– 2023-10-19Decoupling the economy from energy
2021-05-21
– 2023-10-03Material basis of the economy
2011-10-17
– 2023-10-03Brain-like neuronal computation
2015-12-22
– 2023-10-03Power generation, engineering and use
On the magical, terrible requisites of keeping the lights on
2021-05-04
– 2023-09-25Probabilistic numerics
2023-07-13
– 2023-09-25Machine learning for climate systems
2020-04-02
– 2023-09-25Informations
Entropies and other measures of surprise
2011-11-25
– 2023-09-04Annealing in inference
Tempering, cooling, Platt scaling…
2020-09-30
– 2023-09-04Bregman divergences
2023-08-29Mirror descent
2019-12-29
– 2023-08-29Potential theory in probability
Something about harmonic functions or whatever
2020-02-12
– 2023-08-24Gradient descent
First order of business
2014-10-04
– 2023-08-24Materials informatics
Machine learning in condensed matter physics, chemistry and materials science
2023-08-01
– 2023-08-08Gradient steps to an ecology of mind
The best as enemy of the good
2011-11-27
– 2023-08-02Bitter lessons in compute and cleverness
Operationalizing the scaling hypothesis
2021-01-14
– 2023-08-02Learnable coarse-graining
Approximate meso-scale physics
2020-08-24
– 2023-08-01Semidefinite proramming
2019-06-29
– 2023-07-24Optimisation
2014-10-04
– 2023-07-24Neural nets with implicit layers
Also, declarative networks, bi-level optimization and other ingenious uses of the implicit function theorem
2020-12-08
– 2023-06-28Statistical mechanics of statistics
2016-12-01
– 2023-06-02Bayesian model calibration
2017-04-11
– 2023-06-01Neural learning dynamical systems
2018-08-13
– 2023-05-23Differentiable PDE solvers
2017-05-15
– 2023-05-15Model order reduction
2015-03-22
– 2023-04-21ΦFlow
A modern python computational fluid dynamics library for ML research
2022-06-24
– 2023-04-17(Nearly-)Convex relation of nonconvex problems
2018-04-04
– 2023-03-17Symbolic regression
2023-03-14Scaling laws for very large neural nets
Compute/size/data tradeoffs
2021-01-14
– 2023-02-16Predictive coding
Does the model that our brains do bayesian variational prediction make any actual predictions about our brains?
2011-11-27
– 2023-02-09Machine learning for physical sciences
Turbulent mixing at the boundary between disciplines with differing inertia and viscosity
2017-05-15
– 2022-12-07Mind as statistical learner
2020-06-23
– 2022-12-06Deep learning as a dynamical system
2018-08-13
– 2022-10-30The edge of chaos
Computation, evolution, competition and other past-times of faculty
2016-12-01
– 2022-10-30Ensemble Kalman methods for training neural networks
Data assimilation for network weights
2022-09-20Neural nets with implicit layers
Also, declarative networks, bi-level optimization and other ingenious uses of the implicit function theorem
2020-12-08
– 2022-08-09Fun tricks in non-convex optimisation
2014-10-04
– 2022-07-14Hydrology, applied
Rivers, aquifers and other wet things that can flood your house
2020-09-07
– 2022-06-30Overparameterization in large models
Improper learning, benign overfitting, double descent
2018-04-04
– 2022-05-27Emergent spacetime
What are qubits again?
2022-05-26This is a simulation
Can the automaton learn to play the game of life?
2017-04-04
– 2022-04-09Reservoir Computing
2022-03-28Learning with conservation laws, invariances and symmetries
2020-04-11
– 2022-02-25Politics as statistical learner
2022-01-31
– 2022-02-17Ergodicity and mixing
Things that probably happen eventually on average
2011-10-17
– 2022-02-13Neural nets with basis decomposition layers
2021-03-09
– 2022-02-01Running neural nets backwards
2022-01-29Quantum computing
2017-12-14
– 2022-01-21Feedback system identification, linear
2016-07-27
– 2022-01-21Mind as statistical learner
2022-01-09Lévy processes
2017-05-29
– 2021-11-17Random neural networks
2017-02-17
– 2021-10-12Gradient descent at scale
Practical implementation of large optimisations
2021-07-14
– 2021-09-28Algorithmic statistics
Probably also algorithmic information theory
2014-07-25
– 2021-09-26Meta learning
Few-shot learning, learning fast weights, learning to learn
2021-09-16Sequential experiments
Especially multiple sequential experiments
2021-08-04Morphogenesis
2011-01-19
– 2021-07-26Point process intensities and statistical estimation thereof
2016-08-01
– 2021-07-07Energy based models
Inference with kinda-tractable un-normalized densities
2021-06-07Path integral formulations of SDEs
Feynman path integrals, esp for stochastic processes
2021-04-19Differentiable model selection
Differentiable hyperparameter search, and architecture search, and optimisation optimisation by optimisation and so on
2020-09-25
– 2021-04-13Prediction processes
Some kind of weird time series formalism
2021-04-09Why does deep learning work?
Are we in the pocket of Big VRAM?
2017-05-30
– 2020-12-14Observability and sensitivity in learning dynamical systems
Parameter identifiability in dynamical models
2020-11-09Time
Certain quirks of entropy
2020-10-20Adaptive design of experiments
Minesweeper++
2017-04-11
– 2020-10-13AutoML
2017-07-17
– 2020-10-02Monte Carlo optimisation
2020-09-30Emulators and surrogate models via ML
Shortcuts in scientific simulation using ML
2020-08-12
– 2020-08-26Bushfire models
2020-09-07
– 2020-08-19Empirical estimation of information
Informing yourself from your data how informative your data was
2011-04-19
– 2020-04-28Spatial point process and their statistics
2016-08-17
– 2019-12-04Gradient descent, Higher order
2019-10-26Point processes
2016-08-01
– 2019-02-18Sparse stochastic processes identification and sampling
Discrete sample representation of sparse continuous stochastic processes
2018-11-22
– 2018-10-29Pattern formation
2014-12-23
– 2018-10-09Optimisation, combinatorial
2018-08-11Quantum information in physics
What are qubits again?
2017-07-24
– 2017-12-14Gradient descent, continuous, primal/dual formulations.
2017-08-07Thermodynamics of life
2014-09-23
– 2017-05-30Distributed statistica inference
2016-10-11Statistical learning theory
Eventually including structural risk minimisation, risk bounds, hopefully-uniform convergence rates, VC-dimension, generalisation-and-stability framings etc
2016-07-06
– 2016-08-16Dynamical systems
2016-04-26
– 2016-07-27Thermodynamics
2015-02-20
– 2016-01-18Coarse graining
2014-11-11
– 2015-12-02Statistical mechanics
2015-11-14Earthquakes
2015-07-14Complexity
2011-11-25
– 2015-04-11Computational mechanics
2011-10-17
– 2015-01-02Flocking
2011-04-19
– 2014-11-20Econophysics
2011-04-13Simulation for the social sciences
2010-09-28