Courses
Timeline: Spring 2025
Machine Learning for Mechanical Engineering is a field that applies machine learning algorithms to analyze, model, and optimize mechanical systems. It enables data-driven predictions, pattern recognition, and automation in engineering tasks such as design, control, and diagnostics. This integration enhances computational efficiency and decision-making in mechanical engineering applications.
Timeline: Spring 2019, Spring 2018, Spring 2017
Numerical methods for ordinary differential equations are methods used to find numerical approximations to the solutions of ordinary differential equations (ODEs). Their use is also known as "numerical integration", although this term can also refer to the computation of integrals.
Timeline: Fall 2017
Uncertainty quantification (UQ) is the science of quantitative characterization and estimation of uncertainties in both computational and real-world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known.
Timeline: Fall 2018, Fall 2016
The spectral element method combines the geometric flexibility of the classical h-type finite element technique with the desirable numerical properties of spectral methods, employing high-degree piecewise polynomial basis functions on coarse finite element-type meshes.
Timeline: Spring 2016
Computational modeling is the use of computers to simulate and study complex systems using mathematics, physics and computer science. A computational model contains numerous variables that characterize the system being studied.
Manna Workshop (Sandia National Laboratory), Santa Fe, NM, Dec. 2017
Division of Applied Mathematics, Brown University, Aug. 2016
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