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Found 11 papers, 1 papers with code
Schrödinger's control and estimation paradigm with spatio-temporal distributions on graphs
The problem of reconciling a prior probability law with data was introduced by E. Schr\"odinger in 1931/32.
Data Assimilation for Sign-indefinite Priors: A generalization of Sinkhorn's algorithm
The resulting algorithm generalizes the Sinkhorn algorithm in that it amounts to iterative scaling of the entries of the array along different coordinate directions.
Promotion/Inhibition Effects in Networks: A Model with Negative Probabilities
Herein we address the inverse problem to determine network edge-weights based on a sign-indefinite adjacency and expression levels at the nodes.
A matching principle for power transfer in Stochastic Thermodynamics
Gradients in temperature and particle concentration fuel many processes in the physical and biological world.
Monge-Kantorovich Optimal Transport Through Constrictions and Flow-rate Constraints
We quantify flow-rate constraints via a bound on a sought probability density of the times that mass-elements cross toll stations and cast the transportation scheduling in a Kantorovich-type of formalism.
Optimal transport through a toll
We provide a precise formulation which, in addition, is amenable to generalization in higher dimensions.
Geometry of finite-time thermodynamic cycles with anisotropic thermal fluctuations
In contrast to the classical concept of a Carnot engine that alternates contact between heat baths of different temperatures, naturally occurring processes usually harvest energy from anisotropy, being exposed simultaneously to chemical and thermal fluctuations of different intensities.
Regression analysis of distributional data through Multi-Marginal Optimal transport
We formulate and solve a regression problem with time-stamped distributional data.
Stochastic dynamical modeling of turbulent flows
Drawing on this abundance of data, dynamical models can be constructed to reproduce structural and statistical features of turbulent flows, opening the way to the design of effective model-based flow control strategies.
Probabilistic Kernel Support Vector Machines
We propose a probabilistic enhancement of standard kernel Support Vector Machines for binary classification, in order to address the case when, along with given data sets, a description of uncertainty (e. g., error bounds) may be available on each datum.
Proximal algorithms for large-scale statistical modeling and sensor/actuator selection
Several problems in modeling and control of stochastically-driven dynamical systems can be cast as regularized semi-definite programs.
