18 citations · 42 across the 13 of their papers we have counts for
13 papers
Scientific Machine Learning for Engine Health Management and Remaining Useful Life Prediction
Jostein Barry-Straume, Changmin Son, Adrian Sandu +4
Engine Health Management (EHM) depends on reliable forecasting of Remaining Useful Life (RUL) and on tracking thermal indicators such as turbine gas temperature (TGT). In practice,…
Benchmarking Machine Learning Uncertainty Quantification Methodologies for Predicting Turbine Gas Temperature Degradation
Jostein Barry-Straume, Changmin Son, Adrian Sandu +4
Effective prognostics and health management of modern engines relies on accurate turbine gas temperature predictions and robust uncertainty quantification to ensure reliability and…
A Patankar predictor-corrector approach for positivity-preserving time integration
Kamila Nurkhametova, Reid J. Gomillion, Amit N. Subrahmanya +1
Many natural processes, such as chemical reactions and wave dynamics, are modeled as production-destruction (PD) systems that obey positivity and linear conservation laws. Classica…
Ensemble based Closed-Loop Optimal Control using Physics-Informed Neural Networks
Jostein Barry-Straume, Adwait D. Verulkar, Arash Sarshar +2
The objective of designing a control system is to steer a dynamical system with a control signal, guiding it to exhibit the desired behavior. The Hamilton-Jacobi-Bellman (HJB) part…
A copula-based rank histogram ensemble filter
Amit N. Subrahmanya, Julie Bessac, Andrey A. Popov +1
Serial ensemble filters implement triangular probability transport maps to reduce high-dimensional inference problems to sequences of state-by-state univariate inference problems.…
Multirate methods for ordinary differential equations
Michael Günther, Adrian Sandu
This survey provides an overview of state-of-the art multirate schemes, which exploit the different time scales in the dynamics of a differential equation model by adapting the com…
Preserving Nonlinear Constraints in Variational Flow Filtering Data Assimilation
Amit N. Subrahmanya, Andrey A. Popov, Reid J. Gomillion +1
Data assimilation aims to estimate the states of a dynamical system by optimally combining sparse and noisy observations of the physical system with uncertain forecasts produced by…
Improving the Adaptive Moment Estimation (ADAM) stochastic optimizer through an Implicit-Explicit (IMEX) time-stepping approach
Abhinab Bhattacharjee, Andrey A. Popov, Arash Sarshar +1
The Adam optimizer, often used in Machine Learning for neural network training, corresponds to an underlying ordinary differential equation (ODE) in the limit of very small learnin…
Simultaneous Optimal System and Controller Design for Multibody Systems with Joint Friction using Direct Sensitivities
Adwait Verulkar, Corina Sandu, Adrian Sandu +1
Real-world multibody systems are often subject to phenomena like friction, joint clearances, and external events. These phenomena can significantly impact the optimal design of the…
ODE Test Problems: a MATLAB suite of initial value problems
Steven Roberts, Andrey A. Popov, Adrian Sandu
ODE Test Problems (OTP) is an object-oriented MATLAB package offering a broad range of initial value problems which can be used to test numerical methods such as time integration m…
A Bayesian Approach to Multivariate Adaptive Localization in Ensemble-Based Data Assimilation with Time-Dependent Extensions
Andrey A Popov, Adrian Sandu
Ever since its inception, the Ensemble Kalman Filter has elicited many heuristic methods that sought to correct it. One such method is localization---the thought that `nearby' vari…
Solving stochastic chemical kinetics by Metropolis Hastings sampling
Azam S. Zavar Moosavi, Paul Tranquilli, Adrian Sandu
This study considers using Metropolis-Hastings algorithm for stochastic simulation of chemical reactions. The proposed method uses SSA (Stochastic Simulation Algorithm) distributio…
Multirate generalized additive Runge Kutta methods
Michael Guenther, Adrian Sandu
This work constructs a new class of multirate schemes based on the recently developed generalized additive Runge-Kutta (GARK) methods (Sandu and Guenther, 2013). Multirate schemes…