Maziar Raissi
Department of Applied Mathematics, University of California, Riverside
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Our lab’s research lies at the intersection of Scientific Computing and Artificial Intelligence (AI), with a focus on integrating foundational first principles into AI models to address complex challenges in science and engineering. We specialize in developing algorithms that combine data-driven methods with first principles such as the conservation of mass, energy, and momentum. Central to our work is the development and application of Physics-Informed Neural Networks (PINNs), which embed physical laws—described by ordinary and partial differential equations—directly into their architecture. This integration enables efficient modeling and simulation of complex systems where data and physics interact, resulting in solutions that are both accurate and consistent with the laws of nature. Our interdisciplinary approach bridges traditional numerical methods and modern machine learning, enabling transformative applications in computational modeling, biomedical sciences, and engineering.