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MATLAB and Simulink for Physics

Control experiments, acquire and analyze data, and compare with simulations

Physicists worldwide rely on MATLAB^® and Simulink^® to perform both exploratory and computationally demanding simulations. The matrix-oriented computing environment makes MATLAB a natural choice for rapid code development in search of novel physics and collaborating with the industry. MATLAB and Simulink also provide an integrated approach for hardware code generation, data acquisition, real-time simulation and testing, data analysis, and scalable computations.

Physicists choose MATLAB and Simulink to:

Integrate AI methods with workflows to analyze and visualize data
Run particle accelerators
Process signals received by radio telescopes and gravitational wave detectors
Control a variety of “small lab” hardware
Compare simulations with experimental data
Teach physics and share work with other physicists

“For LIGO, we used MATLAB to analyze the fundamental noises that limit gravitational wave detector performance, calculate the optical response of our interferometers, and verify the entire control chain ...”
Matthew Evans, MIT

Using MATLAB and Simulink for Physics

Cancer Diagnostics with Deep Learning and Photonic Time Stretch

Confirming the First-Ever Detection of Gravitational Waves by Analyzing Laser Interferometer Data

Accelerating the Design of Radio Astronomy Instruments in South Africa and Worldwide with Simulink

MATLAB and Simulink for Physics in “Small Labs”

Physicists use MATLAB and Simulink to connect to and control lab hardware, such as custom microscopes, perform various spectroscopic analyses, develop AI-enhanced sensors, and analyze data.

Highly optimized operations on dense and sparse matrices are convenient for rapid code development to simulate classical and quantum many-body systems. Symbolic math enables calculation to arbitrary precision.

Using MATLAB and Simulink, physicists can:

Automatically generate HDL and C/C++ code for hardware connectivity, and use C/C++ code within MATLAB
Control and acquire data in real-time from hardware and instruments
Deploy computations on clusters for big data or demanding calculations
Share codes using intuitive Live Scripts and GUIs
Accelerate AI and other computationally intensive analytics on GPUs
Scale computations to clusters and clouds using MATLAB Parallel Server™
Teach physics using an interactive course curriculum

MATLAB and Simulink for Physics in “Small Labs”

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MATLAB and Simulink for Physics in “Big Labs”

MATLAB and Simulink enable rapid prototyping and modeling real-time control systems for big experiments, such as LIGO. Readability of the code and backward compatibility are particularly attractive features of MATLAB for large, long-term collaborations.

Accelerator physicists use MATLAB to control synchrotrons and Linacs around the globe. With MATLAB they also monitor particle beams and compare beam behavior with simulated versions. MATLAB and some community toolboxes written by accelerator physicists provide a rapid prototyping and deployment system, which has been well-tested world-wide.

MATLAB and Simulink enable physicists and engineers to:

Design filter modules enhanced with AI for noise suppression and signal processing
Eliminate unscheduled “big machine” downtime
Interact with hardware using intuitive scripting and GUI environments
Use code generation for PLCs, FPGAs, and ASICs
Design control systems
Seamlessly transition from desktop simulation to real-time testing with Simulink Real-Time™ and Speedgoat

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MATLAB for Medical Physics

Medical physicists use MATLAB as a unified platform for treatment planning, which is particularly convenient for education and research. With MATLAB, radiation therapists can use semi-automatic labeling tools that facilitate integrating AI methods in workflows for applications such as image guided radiation therapy.

With MATLAB and community toolboxes, medical physicists can:

Produce clinically accurate treatment plans
Plan intensity-modulated radiation therapy for multiple modalities
Preprocess and export data to train deep learning auto-segmentation models, beginning with DICOM or other file formats
Train deep learning models using multi-channel images by various transformations and methods of populating image channels
Perform time domain acoustic and ultrasound simulations in complex and tissue-realistic media

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