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toshj's Projects

18337 icon 18337

18.337 - Parallel Computing and Scientific Machine Learning

18s191 icon 18s191

Course 18.S191 at MIT, Spring 2021 - Introduction to computational thinking with Julia:

3_state_filter icon 3_state_filter

Matlab prototyping of various 3-state yaw estimators using Inertial and GPS data

aerobenchvv icon aerobenchvv

Simulation and analysis tools for autonomous F-16 maneuvers as a V&V benchmark

aerootools icon aerootools

Aircraft Equations of Motion Object-oriented Tools

aerosandbox icon aerosandbox

Aircraft design optimization made fast through modern automatic differentiation. Written in Python, work in progress.

aviation-2019-code icon aviation-2019-code

Code of article "Collocation-Based Output-Error Method for Aircraft System Identification" presented in the 2019 AIAA Aviation Forum - Flight Test Conference

bisosprob icon bisosprob

Toolbox for bilinear sum-of-squares problems.

c130ins icon c130ins

evaluation of the "hybrid" INS variables from the C-130 INS.

cbf-clf-helper icon cbf-clf-helper

Matlab Interface for Control Barrier Function (CBF) and Control Lyapunov Function (CLF) based control methods.

cdmdc icon cdmdc

Compressive dynamic mode decomposition with control for compressive system identification

clf-cbf-qp icon clf-cbf-qp

Matlab class/functions to simulate a system implementing a control lyapunov-control barrier function quadratic program controller

convex-util icon convex-util

This is a collection of unsorted, unthemed, and presumably untested MATLAB utility functions for my CONVEX PhD thesis.

copper-2021 icon copper-2021

System identification of copper electrodissolution in a solution of phosphoric acid

databook_matlab icon databook_matlab

Matlab files with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz http://www.databookuw.com/

databook_python icon databook_python

IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz

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