8 citations · 13 across the 5 of their papers we have counts for
5 papers
On Learning what to Learn: heterogeneous observations of dynamics and establishing (possibly causal) relations among them
David W. Sroczynski, Felix Dietrich, Eleni D. Koronaki +4
Before we attempt to learn a function between two (sets of) observables of a physical process, we must first decide what the inputs and what the outputs of the desired function are…
Estimating position-dependent and anisotropic diffusivity tensors from molecular dynamics trajectories: Existing methods and future outlook
Tiago Domingues, Ronald Coifman, Amir Haji-Akbari
Confinement can substantially alter the physicochemical properties of materials by breaking translational isotropy and rendering all physical properties position-dependent. Molecul…
Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning
Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne +1
Finding meaningful representations and distances of hierarchical data is important in many fields. This paper presents a new method for hierarchical data embedding and distance. Ou…
No equations, no parameters, no variables: data, and the reconstruction of normal forms by learning informed observation geometries
Or Yair, Ronen Talmon, Ronald R. Coifman +1
The discovery of physical laws consistent with empirical observations lies at the heart of (applied) science and engineering. These laws typically take the form of nonlinear differ…
Data-Driven Reduction for Multiscale Stochastic Dynamical Systems
Carmeline J. Dsilva, Ronen Talmon, C. William Gear +2
Multiple time scale stochastic dynamical systems are ubiquitous in science and engineering, and the reduction of such systems and their models to only their slow components is ofte…