Mesbah Lab

LB-Multi-Stage-NMPC

Learning-based multi-stage NMPC algorithm with guarantees on feasibility using robust control invariant sets

DFT-microkinetic

A Study on the Role of Electric Field in Low-Temperature Plasma Catalytic Ammonia Synthesis via Integrated Density Functional Theory and Microkinetic Modeling

ML-for-plasmas

This repository contains code that demonstrates the use of a variety of machine learning strategies for low temperature plasma systems.

nsPCE-toolbox

This code is a methods toolbox for constructing non-smooth polynomial chaos expansion (nsPCE) surrogate models. The codes for the nsPCE framework are applicable to non-smooth ODE models and particularly for dynamic flux balance analysis (DFBA) models.

SPINODE

This code trains and implements a stochastic physics-informed neural ordinary differential equation (SPINODE) framework on a directed colloidal self-assembly test case.

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arbo-controllers

Here we visualize the need for robust BO against an adversary. Clearly the optimum design point changes depending the uncertain parameter x, so we should identify a region for which the decision variable x resides in an optimal region.

Berkeley-Lam-2023-UNLOCK

Lam collaboration for offset-free filter of model predictive control on CAPPJ system

BO4Policy_Search_Plasma

Towards Personalized Plasma Medicine via Data-efficient Adaptation of Fast Deep Learning-based MPC Policies

CAP-Sensor4Bio

Machine-Learning-for-Plasma-Diagnostics

This code trains and implements machine learning models for real-time diagnostics of cold atmospheric plasma sources.

Performance-Oriented-DNN-MPC

Performance-oriented model learning for control via multi-objective Bayesian optimization

SafeBOPlasma

Safe Explorative Bayesian Optimization -- Towards Personalized Treatments in Plasma Medicine

SNSF-project-P2ELP2_184521

Multivariable control strategy for a reactor system, efficient global solution method for a reaction system and rocket, solution methods for two approximate formulations of the Bayesian optimal experiment design (OED) problem, optimal control approach for a cold plasma system.

APPJ-MacOS-Communication

Control of the CAPPJ system using MacOS

Code-for-AL-PSST-Mesbah-lab

Active learning-guided exploration of parameter space of air plasmas to enhance the energy efficiency of NO x production

colloid_char

This code trains and implements a characterization framework based on deep learning for characterizing structural states of colloidal self-assembly systems.

DNN_MPC_Plasma_FPGA

Project files for a neural network (NN) implementation on an FPGA using Vivado HLS.

HW-SW_CoDesign4CoC

Code repository for the paper on A Practical Multi-Objective Learning Framework for Optimal Hardware-Software Co-Design of Control-on-a-Chip Systems by Kimberly J. Chan, Joel A. Paulson, and Ali Mesbah.

LCSS_DataDrivenScenarioOptimization

This code obtains closed-loop performance guarantees for automated controller tuning, which can be formulated as a black-box optimization problem under uncertainty.

Mesbah-APPJ

This is a cleaned-up version of APPJ_Control which was forked from Dogan Gidon's APPJ_Control repository. This will be the location of the latest info (until August 2024) about the atmospheric pressure plasma jet (APPJ) located in the Mesbah Lab at UC Berkeley. The repository contains condensed information from the previous iterations.

Plasma-Wafers

Code for running APPJ experiments with silicon wafers

PlasmaRL-APPJ

This code trains and implements a reinforcement learning framework for control of the thermal effects of an atmospheric pressure plasma jet