sciml

System identification in continuous time Learning in continuous ODEs, SDEs and CDEs 2016-08-01 – 2023-11-30

Simulation-based inference 2014-12-23 – 2023-11-23

Multi fidelity models Data-driven multi-scale sampling, multi-resolution, super-resolution 2020-08-24 – 2023-11-20

Machine learning for partial differential equations 2017-05-15 – 2023-11-17

Feedback system identification, not necessarily linear Learning dynamics from data 2016-08-01 – 2023-11-15

Numerical PDE solvers 2016-03-01 – 2023-10-23

Physics-informed neural networks 2019-10-15 – 2023-10-19

Neural PDE operator learning Especially forward operators. Image-to-image regression, where the images encode a physical process. 2019-10-15 – 2023-10-19

Probabilistic numerics 2023-07-13 – 2023-09-25

Machine learning for climate systems 2020-04-02 – 2023-09-25

Materials informatics Machine learning in condensed matter physics, chemistry and materials science 2023-08-01 – 2023-08-08

Neural nets with implicit layers Also, declarative networks, bi-level optimization and other ingenious uses of the implicit function theorem 2020-12-08 – 2023-06-28

Statistical mechanics of statistics 2016-12-01 – 2023-06-02

Neural learning dynamical systems 2018-08-13 – 2023-05-23

Differentiable PDE solvers 2017-05-15 – 2023-05-15

Reparameterization methods for MC gradient estimation Pathwise gradient estimation, 2018-04-04 – 2023-05-02

Particle filters incorporating Interacting Particle Systems, Sequential Monte Carlo and a profusion of other simultaneous-discovery names 2014-07-25 – 2023-03-24

Deep sets invariant and equivariant functions 2022-11-24 – 2023-03-21

Symbolic regression 2023-03-14

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

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

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

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

Neural learning for spatiotemporal systems 2020-09-16 – 2022-07-28

System identification using particle filters A.k.a. parameter estimation in data assimilation 2014-07-25 – 2022-05-04

Particle belief propagation Graphical inference using empirical distribution estimates 2014-07-25 – 2022-04-08

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

Feedback system identification, linear 2016-07-27 – 2022-01-21

Pyro Approximate maximum in the density of probabilistic programming effort 2019-10-02 – 2021-11-25

Gaussian Processes as stochastic differential equations Imposing time on things 2019-09-18 – 2021-11-25

Learning summary statistics 2020-04-22 – 2021-07-15

Stochastic differential equations 2019-09-19 – 2021-06-22

Probabilistic spectral analysis 2019-11-13 – 2020-11-25

Optimal control 2015-06-22 – 2019-11-01

Defining dynamics via Gaussian processes 2019-09-18

Sparse stochastic processes identification and sampling Discrete sample representation of sparse continuous stochastic processes 2018-11-22 – 2018-10-29

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

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