111 - 120 of 184 results for: ME

High Performance Computing (HPC) powers modern artificial intelligence, enabling the scale required for deep learning, generative AI, and emerging agentic systems. This course introduces the design and use of HPC clusters for AI applications in academia and industry. Students explore the AI lifecycle - from data collection to advanced deep learning - while connecting high-level concepts to systems-level implementation. Topics include GPU/TPU architectures, parallel computing, cluster operating systems, resource management, containerization, and advanced methods such as attention and mixture-of-experts. Hands-on labs emphasize building and configuring HPC systems, optimizing networks, managing resources, and deploying AI workloads in computer vision and natural language processing. The course also features instruction on hyperscale data centers from an industry expert. Students gain practical experience in both AI development and the infrastructure required to run it at scale. Open to students from diverse backgrounds.

Get ready to explore the future of high-performance computing (HPC) and artificial intelligence (AI) and its influence on the way we live, work and learn, with the HPC-AI Summer Seminar Series by Stanford High Performance Computing Center and the HPC-AI Advisory Council. This 1-unit course is designed to provide practical insights and thought leadership and discuss topics of great societal importance. One such theme this year is the impact of Generative AI. You will have the opportunity to hear from renowned industry experts and influencers who are shaping our HPC-AI future and even ask them your questions. This engaging course is open to students with any academic background looking to upskill themselves. So don't hesitate, register now! No prerequisites required.

The mechanism and occurrences of fatigue of materials. Methods for predicting fatigue life and for protecting against premature fatigue failure. Use of elastic stress and elastic-plastic strain analyses to predict crack initiation life. Use of linear elastic fracture mechanics to predict crack propagation life. Effects of stress concentrations, manufacturing processes, load sequence, irregular loading, multi-axial loading. Subject is treated from the viewpoints of the engineer seeking up-to-date methods of life prediction and the researcher interested in improving understanding of fatigue behavior. Prerequisite: undergraduate mechanics of materials.

The main purpose of this course is to provide students with enough statistical mechanics background to the Molecular Simulations classes ( ME 346B,C), including the fundamental concepts such as ensemble, entropy, and free energy, etc. The main theme of this course is how the laws at the macroscale (thermodynamics) can be obtained by analyzing the spontaneous fluctuations at the microscale (dynamics of molecules). Topics include thermodynamics, probability theory, information entropy, statistical ensembles, phase transition and phase equilibrium. Recommended: PHYSICS 110 or equivalent.

Algorithms of molecular simulations and underlying theories. Molecular dynamics, time integrators, modeling thermodynamic ensembles (NPT, NVT). Monte Carlo simulations. Applications in solids, liquids, polymers, phase transitions, and combination with machine learning tools. Examples provided in easy-to-use Python Notebooks. Final projects.

Wave propagation and sources in elastic solids and compressible fluids; body, surface, and interface waves in homogeneous and plane layered media; dispersion, phase and group velocities; reflection and transmission; near-field, far-field, and static limits; effects of gravity, surface and internal gravity waves; Fourier methods and solutions in the time and frequency domains; Green's functions; reciprocity; adjoint methods and full-waveform inversion; point and line sources, finite sources, moving sources and directivity effects; multipole expansions; source representation in solids using transformation strain; application to earthquakes, volcanoes, and tsunamis. Prerequisites: Graduate-level background in continuum mechanics.

Physical and metallurgical principles involved in metal 3D printing: laser types and optics, light interaction with matter, melt pool dynamics, solidification and microstructure generation. Engineering practice: powder preparation, part characterization, material printing strategy exploration, simulation tools for innovative designs and process physical modeling. Some of the lectures will be delivered by leading experts in industry to highlight current challenges and opportunities. Students design, prepare and print a part in the laboratory part of the class. Prerequisite: an UG degree in ME or Materials Science.

Guest speakers present research related to plasma science and engineering, ranging from fundamental plasma physics to industrial applications of plasma.

Exact and approximate analysis of fluid flow covering kinematics, global and differential equations of mass, momentum, and energy conservation. Forces and stresses in fluids. Euler's equations and the Bernoulli theorem applied to inviscid flows. Vorticity dynamics. Topics in irrotational flow: stream function and velocity potential for exact and approximate solutions; superposition of solutions; complex potential function; circulation and lift. Some boundary layer concepts.