111 - 120 of 195 results for: ME

This course is designed to provide an introduction to discontinuous Galerkin (DG) methods and related high-order discontinuous solution techniques for solving partial differential equations with application to fluid flows. The course covers mathematical and theoretical concepts of the DG-methods and connections to finite-element and finite-volume methods. Computational aspects on the discretization, stabilization methods, flux-evaluations, and integration techniques will be discussed. Problems and examples will be drawn from advection-reaction-diffusion equations, non-linear Euler and Navier-Stokes systems, and related fluid-dynamics problems. As part of a series of homework assignments and projects, students will develop their own DG-method for solving the compressible flow equations in complex two-dimensional geometries.

Growth is a distinguishing feature of all living things. This course introduces the concept of living systems through the lens of mechanics. We discuss the basic continuum theory for living systems including the kinematics, balance equations, and constitutive equations and the computational modeling of growth phenomena including growing plants, remodeling bone, healing wounds, growing tumors, atherosclerosis, expanding skin, failing hearts, developing brains, and the effects of high performance training.

Introduction to vectors and tensors: kinematics, deformation, forces, and stress concept of continua; balance principles; aspects of objectivity; hyperelastic materials; thermodynamics of materials; variational principles.

This class will give hands-on experience with programming multicore processors, graphics processing units (GPU), and parallel computers. The focus will be on the message passing interface (MPI, parallel clusters) and the compute unified device architecture (CUDA, GPU). Topics will include multithreaded programs, GPU computing, computer cluster programming, C++ threads, OpenMP, CUDA, and MPI. Pre-requisites include C++, templates, debugging, UNIX, makefile, numerical algorithms (differential equations, linear algebra).

Introduction to the theories of elasticity, plasticity and fracture and their applications. Elasticity: Definition of stress, strain, and elastic energy; equilibrium and compatibility conditions; and formulation of boundary value problems. Stress function approach to solve 2D elasticity problems and Green¿s function approach in 3D. Applications to contact and crack. Plasticity: Yield surface, associative flow rule, strain hardening models, crystal plasticity models. Applications to plastic bending, torsion and pressure vessels. Fracture: Linear elastic fracture mechanics, J-integral, Dugdale-Barrenblatt crack model. Applications to brittle fracture and fatigue crack growth. Computer programming in Matlab is used to aid analytic derivation and numerical solutions.

Cell mechanobiology topics including cell structure, mechanical models, and chemo-mechanical signaling. Review and apply methods for controlling and analyzing the biomechanics of cells using traction force microscopy, AFM, micropatterning and cell stimulation. Practice and theory for the design and application of methods for quantitative cell mechanobiology.

Linear and kernel support vector machines, deep learning, deep neural networks, generative adversarial networks, physics-based machine learning, forward and reverse mode automatic differentiation, optimization algorithms for machine learning, TensorFlow, PyTorch.

High performance computing (HPC) is a field at the forefront of a range of high tech applications such as computational fluid dynamics, image processing, and financial risk management. With the demands of machine learning outstripping conventional computing, HPC is also at the forefront of artificial intelligence. This course will discuss how HPC clusters are used in large-scale problems in academia and industry alike. Students will learn about HPC clusters from the ground up and gain a solid foundation in parallel computer architectures, cluster operating systems, resource management, and containers. They will build their own systems via remote installation of physical hardware, configuration and optimization of a high-speed network, and integration of other technologies used throughout the HPC world. Classes consist of lectures reinforced with assignments on HPC systems located in a teaching laboratory, where discussion and collaboration will be key components of the course. Students will come away with a solid skill set in a field of computing that has broad implications for science and technology.

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.