Data-driven Modeling and Probabilistic Scientific Computing
ENM 5310
Paris Perdikaris
Tu, Thurs (3:30-5pm)
Towne Building

We will revisit classical scientific computing from a statistical learning viewpoint. In this new computing paradigm, differential equations, conservation laws, and data act as complementary agents in a predictive modeling pipeline. This course aims explore the potential of modern machine learning as a unifying computational tool that enables learning models from experimental data, inferring solutions to differential equations, blending information from a hierarchy of models, quantifying uncertainty in computations, and efficiently optimizing complex engineering systems.

Introduction to Data-driven Modeling
ENM 3600
Paris Perdikaris
Tu, Thurs (3:30-5pm)
Towne Building

From recognizing voice, text or images to designing more efficient airplane wings and discovering new drugs, machine learning is introducing a transformative set of tools in data analysis with increasing impact across engineering, sciences, and commercial applications. In this course, you will learn about principles and algorithms for extracting patterns from data and and making effective automated predictions. We will cover concepts such as regression, classification, density estimation, feature extraction, sampling, and probabilistic modeling, and provide a formal understanding of how, why, and when these methods work in the context of analyzing physical, biological, and engineering systems.

AI for Science and Engineering - From Data to Discovery
MEAM 4600
Paris Perdikaris
Tu, Thurs (3:30-5pm)
Towne Building

Embark on an exciting journey into the world of AI-driven scientific discovery and engineering innovation. This course introduces cutting-edge machine learning techniques tailored for science and engineering applications. From enhancing material properties to revolutionizing drug discovery, from optimizing renewable energy systems to advancing robotics, AI is reshaping how we approach complex problems across disciplines. You'll learn to harness the power of data to extract meaningful patterns, make accurate predictions, and gain insights into physical, biological, and engineering systems. Through hands-on projects and real-world case studies, you'll develop the skills to apply state-of-the-art AI tools to solve pressing challenges in your field, positioning yourself at the forefront of the AI revolution in science and engineering.