The paper on Stochastic physics-informed neural ordinary differential equations has been accepted for publication in the Journal of Computational Physics.
Journal Publication
The paper on A Deep Learning Framework Discovers Compositional Order and Self-Assembly Pathways in Binary Colloidal Mixtures has been published in JACS Au.
Journal Publication
The paper on Space Bioprocess Engineering on the Horizon has been published in Nature’s Communications Engineering.
Magazine Feature
Ali and George’s work in creating a framework to address the integration of biomanufacturing systems in space is featured in an article in the Spring/Summer 2022 Edition of Catalyst. The aim of their work is to advance learning-based predictive control methods with a view to optimize integrated biomanufacturing systems in real-time!
Journal Publication
The paper on Tractable global solutions to chance-constrained Bayesian optimal experiment design for arbitrary prior and noise distributions has been published in the Journal of Process Control.
Workshop Talk
Kimberly has been selected to give a talk on "Automated Tuning of Generic Embedded Controllers using Multi-objective Bayesian Optimization” at the 4th NorCal Control Workshop at the University of California, Santa Cruz.
Journal Publication
The paper on Data-Driven Adaptive Optimal Control Under Model Uncertainty: An Application to Cold Atmospheric Plasmas has been accepted for publication in IEEE Transactions on Control Systems Technology.
Journal Publication
The paper on Active Learning-guided Exploration of Parameter Space of Air Plasmas to Enhance the Energy Efficiency of NOx Production has been accepted for publication in Plasma Sources Science and Technology.
Journal Publication
The paper on Efficient Global Solutions to Single-Input Optimal Control Problems via Approximation by Sum-of-Squares Polynomials has been accepted for publication in IEEE Transactions on Automatic Control.
Journal Publication
The paper on Performance-Oriented Model Learning for Control via Multi-Objective Bayesian Optimization has been accepted for publication in Computers & Chemical Engineering.
Preprint
The paper on Data-Driven Flow-Map Models for Data-Efficient Discovery of Dynamics and Fast Uncertainty Quantification of Biological and Biochemical Systems has been posted on bioRxiv.
Journal Publication
The paper on Adversarially Robust Bayesian Optimization for Efficient Auto-Tuning of Generic Control Structures under Uncertainty has been accepted for publication in the AIChE Journal.
Journal Publication
The paper on Low Temperature Plasma for Biology, Hygiene, and Medicine: Perspective and Roadmap has been accepted for publication in IEEE Transactions on Radiation and Plasma Medical Sciences.
Welcome Thomas and Mira!
Thomas Chang-Davidson and Mira Khare have joined the Mesbah Lab as Graduate Student Researchers! Welcome Thomas and Mira!
Journal Publication
The paper on Multivariable Control Based on Incomplete Models via Feedback Linearization and Continuous-Time Derivative Estimation has been accepted for publication in the International Journal of Robust and Nonlinear Control.
Invited Papers
The invited papers Probabilistically Robust Bayesian Optimization for Data-Driven Design of Arbitrary Controllers with Gaussian Process Emulators and On the Stability Properties of Perception-aware Chance-constrained MPC in Uncertain Environments have been accepted for presentation at the IEEE Conference on Decision and Control.
Journal Publication
The paper on Towards a Biomanufactory in Mars has been accepted for publication in Frontiers in Astronomy and Space Sciences.
Conference Publications
The papers on Data-Driven Scenario Optimization for Automated Controller Tuning with Probabilistic Performance Guarantees, Deep Learning-Based Approximate Nonlinear Model Predictive Control with Offset-Free Tracking for Embedded Applications, and Perception-Aware Chance-Constrained Model Predictive Control for Uncertain Environments have been accepted for presentation at the 2021 American Control Conference.
The papers on A Data-Driven Automatic Tuning Method for MPC Under Uncertainty Using Constrained Bayesian Optimization and An Adaptive Correction Scheme for Offset-Free Asymptotic Performance in Deep Learning-Based Economic MPC have been accepted for presentation at the 2021 IFAC Symposium on Advanced Control of Chemical Processes (ADChem).
Congratulations Jared!
Jared was selected as a finalist of the CAST Directors’ Student Presentation Awards to present his work “Physics-Constrained Deep Learning of Unmodeled Physics in Systems Governed by Stochastic Differential Equations” during the 2021 AIChE Annual Meeting.
Journal Publication
The paper on Critical Examination of Clearance Concepts for Revealing the Potential for Errors in Clinical Drug Dosing Decisions has been accepted for publication in American Association of Pharmaceutical Scientists Journal.