Search Results for author:
Found 18 papers, 9 papers with code
Neural parameter calibration and uncertainty quantification for epidemic forecasting
The recent COVID-19 pandemic has thrown the importance of accurately forecasting contagion dynamics and learning infection parameters into sharp focus.
Understanding recent deep-learning techniques for identifying collective variables of molecular dynamics
High-dimensional metastable molecular system can often be characterised by a few features of the system, i. e. collective variables (CVs).
Impact Study of Numerical Discretization Accuracy on Parameter Reconstructions and Model Parameter Distributions
In optical nano metrology numerical models are used widely for parameter reconstructions.
Data-driven modelling of nonlinear dynamics by barycentric coordinates and memory
We present a numerical method to model dynamical systems from data.
Statistical Analysis of Tipping Pathways in Agent-Based Models
Agent-based models are a natural choice for modeling complex social systems.
Dimensionality Reduction
Physics and Society
CINDy: Conditional gradient-based Identification of Non-linear Dynamics -- Noise-robust recovery
Governing equations are essential to the study of nonlinear dynamics, often enabling the prediction of previously unseen behaviors as well as the inclusion into control strategies.
Dynamical Systems Applications
Data-driven model reduction of agent-based systems using the Koopman generator
The dynamical behavior of social systems can be described by agent-based models.
Feature space approximation for kernel-based supervised learning
We propose a method for the approximation of high- or even infinite-dimensional feature vectors, which play an important role in supervised learning.
Coupling particle-based reaction-diffusion simulations with reservoirs mediated by reaction-diffusion PDEs
In this work, we develop modeling and numerical schemes for particle-based reaction-diffusion in an open setting, where the reservoirs are mediated by reaction-diffusion PDEs.
Quantitative Methods Chemical Physics Computational Physics 92C40, 92C45, 60J70, 60Gxx, 70Lxx
Kernel Autocovariance Operators of Stationary Processes: Estimation and Convergence
We consider autocovariance operators of a stationary stochastic process on a Polish space that is embedded into a reproducing kernel Hilbert space.
Extending Transition Path Theory: Periodically-Driven and Finite-Time Dynamics
Given two distinct subsets $A, B$ in the state space of some dynamical system, Transition Path Theory (TPT) was successfully used to describe the statistical behavior of transitions from $A$ to $B$ in the ergodic limit of the stationary system.
Dynamical Systems 60J22, 82C26, 60J45, 60J10
Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
We derive a data-driven method for the approximation of the Koopman generator called gEDMD, which can be regarded as a straightforward extension of EDMD (extended dynamic mode decomposition).
Dimensionality Reduction of Complex Metastable Systems via Kernel Embeddings of Transition Manifolds
We present a novel kernel-based machine learning algorithm for identifying the low-dimensional geometry of the effective dynamics of high-dimensional multiscale stochastic systems.
Learning chemical reaction networks from trajectory data
We develop a data-driven method to learn chemical reaction networks from trajectory data.
Optimization and Control 92C42, 62M86
A kernel-based approach to molecular conformation analysis
We present a novel machine learning approach to understanding conformation dynamics of biomolecules.
Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces
Reproducing kernel Hilbert spaces (RKHSs) play an important role in many statistics and machine learning applications ranging from support vector machines to Gaussian processes and kernel embeddings of distributions.
Objective Priors in the Empirical Bayes Framework
Motivated by this principle and following an information-theoretic approach similar to the construction of reference priors, we suggest a penalty term that guarantees this kind of invariance.
Methodology 62G07
Sparse Proteomics Analysis - A compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data
Results: We present a new algorithm, Sparse Proteomics Analysis (SPA), based on the theory of compressed sensing that allows us to identify a minimal discriminating set of features from mass spectrometry data-sets.
