# sciml

System identification in continuous time
Learning in continuous ODEs, SDEs and CDEs
2016-08-01
– 2023-11-30Simulation-based inference
2014-12-23
– 2023-11-23Multi 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
– 2023-11-15Numerical PDE solvers
2016-03-01
– 2023-10-23Physics-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
– 2023-10-19Probabilistic numerics
2023-07-13
– 2023-09-25Machine learning for climate systems
2020-04-02
– 2023-09-25Materials informatics
Machine learning in condensed matter physics, chemistry and materials science
2023-08-01
– 2023-08-08Neural 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
– 2023-06-02Neural learning dynamical systems
2018-08-13
– 2023-05-23Differentiable PDE solvers
2017-05-15
– 2023-05-15Reparameterization methods for MC gradient estimation
Pathwise gradient estimation,
2018-04-04
– 2023-05-02Particle filters
incorporating Interacting Particle Systems, Sequential Monte Carlo and a profusion of other simultaneous-discovery names
2014-07-25
– 2023-03-24Deep sets
invariant and equivariant functions
2022-11-24
– 2023-03-21Symbolic regression
2023-03-14Machine learning for physical sciences
Turbulent mixing at the boundary between disciplines with differing inertia and viscosity
2017-05-15
– 2022-12-07Deep learning as a dynamical system
2018-08-13
– 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
– 2022-08-09Neural learning for spatiotemporal systems
2020-09-16
– 2022-07-28System identification using particle filters
A.k.a. parameter estimation in data assimilation
2014-07-25
– 2022-05-04Particle belief propagation
Graphical inference using empirical distribution estimates
2014-07-25
– 2022-04-08Learning with conservation laws, invariances and symmetries
2020-04-11
– 2022-02-25Feedback system identification, linear
2016-07-27
– 2022-01-21Pyro
Approximate maximum in the density of probabilistic programming effort
2019-10-02
– 2021-11-25Gaussian Processes as stochastic differential equations
Imposing time on things
2019-09-18
– 2021-11-25Learning summary statistics
2020-04-22
– 2021-07-15Stochastic differential equations
2019-09-19
– 2021-06-22Probabilistic spectral analysis
2019-11-13
– 2020-11-25Optimal control
2015-06-22
– 2019-11-01Defining dynamics via Gaussian processes
2019-09-18Sparse stochastic processes identification and sampling
Discrete sample representation of sparse continuous stochastic processes
2018-11-22
– 2018-10-29Dynamical systems
2016-04-26
– 2016-07-27Coarse graining
2014-11-11
– 2015-12-02