Learning stability certificates from data
Many existing tools in nonlinear control theory for establishing stability or safety of a
dynamical system can be distilled to the construction of a certificate function which …
dynamical system can be distilled to the construction of a certificate function which …
Learning dynamical systems from data: a simple cross-validation perspective, part I: parametric kernel flows
Regressing the vector field of a dynamical system from a finite number of observed states is
a natural way to learn surrogate models for such systems. We present variants of cross …
a natural way to learn surrogate models for such systems. We present variants of cross …
Application of data‐driven methods in power systems analysis and control
O Bertozzi, HR Chamorro… - IET Energy Systems …, 2023 - Wiley Online Library
The increasing integration of variable renewable energy resources through power
electronics has brought about substantial changes in the structure and dynamics of modern …
electronics has brought about substantial changes in the structure and dynamics of modern …
OnsagerNet: Learning stable and interpretable dynamics using a generalized Onsager principle
We propose a systematic method for learning stable and physically interpretable dynamical
models using sampled trajectory data from physical processes based on a generalized …
models using sampled trajectory data from physical processes based on a generalized …
On the sample complexity of stability constrained imitation learning
We study the following question in the context of imitation learning for continuous control:
how are the underlying stability properties of an expert policy reflected in the sample …
how are the underlying stability properties of an expert policy reflected in the sample …
Learning region of attraction for nonlinear systems
Estimating the region of attraction (ROA) of general nonlinear autonomous systems remains
a challenging problem and requires a case-by-case analysis. Leveraging the universal …
a challenging problem and requires a case-by-case analysis. Leveraging the universal …
Simple, low-cost and accurate data-driven geophysical forecasting with learned kernels
Modelling geophysical processes as low-dimensional dynamical systems and regressing
their vector field from data is a promising approach for learning emulators of such systems …
their vector field from data is a promising approach for learning emulators of such systems …
One-shot learning of stochastic differential equations with data adapted kernels
We consider the problem of learning Stochastic Differential Equations of the form d X t= f (X
t) d t+ σ (X t) d W t from one sample trajectory. This problem is more challenging than …
t) d t+ σ (X t) d W t from one sample trajectory. This problem is more challenging than …
[HTML][HTML] Kernel-based approximation of the Koopman generator and Schrödinger operator
Many dimensionality and model reduction techniques rely on estimating dominant
eigenfunctions of associated dynamical operators from data. Important examples include the …
eigenfunctions of associated dynamical operators from data. Important examples include the …
[HTML][HTML] Learning dynamical systems from data: A simple cross-validation perspective, part iv: case with partial observations
A simple and interpretable way to learn a dynamical system from data is to interpolate its
governing equations with a kernel. In particular, this strategy is highly efficient (both in terms …
governing equations with a kernel. In particular, this strategy is highly efficient (both in terms …