Search Results for author:
Found 9 papers, 4 papers with code
Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling
The results show that using the Bayesian posterior as a training objective can yield upwards of 724 times improvement in Hamiltonian mean squared error using training data with up to 10% multiplicative noise compared to a standard training objective.
Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds
This work presents two novel approaches for the symplectic model reduction of high-dimensional Hamiltonian systems using data-driven quadratic manifolds.
Bayesian Identification of Nonseparable Hamiltonian Systems Using Stochastic Dynamic Models
This paper proposes a probabilistic Bayesian formulation for system identification (ID) and estimation of nonseparable Hamiltonian systems using stochastic dynamic models.
Bayesian Parameter Estimation for Dynamical Models in Systems Biology
In this study, we propose a comprehensive framework for Bayesian parameter estimation and complete quantification of the effects of uncertainties in the data and models.
Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference
Operator inference learns low-dimensional dynamical-system models with polynomial nonlinear terms from trajectories of high-dimensional physical systems (non-intrusive model reduction).
Certifiable Risk-Based Engineering Design Optimization
CRiBDO is contrasted with reliability-based design optimization (RBDO), where uncertainties are accounted for via the probability of failure, through a structural and a thermal design problem.
Optimization and Control Computational Engineering, Finance, and Science Data Analysis, Statistics and Probability Computation
Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms
The proposed method learns operators for the linear and polynomially nonlinear dynamics via a least-squares problem, where the given non-polynomial terms are incorporated in the right-hand side.
Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems
The lifting map is applied to data obtained by evaluating a model for the original nonlinear system.
BIG-bench Machine Learning
Physics-informed machine learning
Learning physics-based reduced-order models for a single-injector combustion process
The machine learning perspective brings the flexibility to use transformed physical variables to define the POD basis.
