Stanford AA222 / CS361 Engineering Design Optimization I Linear Constrained Optimization

preview_player
Показать описание
April 25, 2024
Joshua Ott of Stanford University

This course covers the design of engineering systems within a formal optimization framework. This course covers the mathematical and algorithmic fundamentals of optimization, including derivative and derivative-free approaches for both linear and non-linear problems, with an emphasis on multidisciplinary design optimization. Topics will also include quantitative methodologies for addressing various challenges, such as accommodating multiple objectives, automating differentiation, handling uncertainty in evaluations, selecting design points for experimentation, and principled methods for optimization when evaluations are expensive. Applications range from the design of aircraft to automated vehicles.

  1. Stanford Online
  2. Stanford
  3. Stanford Online
Рекомендации по теме
Stanford AA222 / 1:19:31

Stanford AA222 / CS361 Engineering Design Optimization I Linear Constrained Optimization

SDSF 2021: Reexamining 0:24:47

SDSF 2021: Reexamining the Logical Foundation of Engineering Decision Making Under Uncertainty

A Data-Driven Design 0:08:40

A Data-Driven Design Pipeline for the Structural Optimization of Quadcopters

Yoram Singer (Princeton) 0:52:44

Yoram Singer (Princeton) -- Memory-Efficient Adaptive Optimization for Humongous-Scale Learning

Fusheng Bai: Surrogate 0:30:52

Fusheng Bai: Surrogate Optimization for Computationally Expensive Black box Functions

From Sequential to 0:16:44

From Sequential to Parallel, a story about Bayesian Hyperparameter Optimization - Andres Asaravicius

Unknown Unknowns in 0:08:47

Unknown Unknowns in Engineering Design

AFMS Webinar 2022 0:58:49

AFMS Webinar 2022 #29 - Dr Miguel Fosas de Pando (Universidad de Cádiz)

Robustness - Optimization 0:13:56

Robustness - Optimization Under Uncertainty

A lightweight digital 0:01:53

A lightweight digital twin of a crane boom based on a multi-fidelity surrogate model

Risk-Averse Stochastic Optimization 0:14:36

Risk-Averse Stochastic Optimization Theory, Methods, and Applications

Bi-Fidelity Expensive Black-Box 0:29:03

Bi-Fidelity Expensive Black-Box problems, Nicolau Andres Thio

Accelerating Scientific Machine 0:08:49

Accelerating Scientific Machine Learning with Automatic Differentiable Surrogates - Ludovico Bessi

Mechanical Engineer - 0:03:05

Mechanical Engineer - Reduce Costs with Design Optimization

Lesia Mitridati: Stochastic 0:45:37

Lesia Mitridati: Stochastic and Data-Driven Optimization (1/2)

Robust Design by 0:21:42

Robust Design by Optimization under Reliability Constraints

Probabilistic design 0:02:10

Probabilistic design

Composite Bayesian Optimization 0:57:37

Composite Bayesian Optimization by Raul Astudillo

Tina Eliassi-Rad - 0:48:09

Tina Eliassi-Rad - The Pitfalls of Using ML-based Optimization - IPAM at UCLA

Surrogate Modeling: Enhancing 0:05:09

Surrogate Modeling: Enhancing Analysis and Optimization through Efficient Approximations

Optimization in Machine 0:22:16

Optimization in Machine Learning - Bayesian Optimization - Important Surrogate Models

Surrogate modelling - 0:17:50

Surrogate modelling - Choosing the right tool for the job

Interpretable Machine Learning 0:14:47

Interpretable Machine Learning - Local Interpretable Model-agnostic Explanations (LIME) - Intro

Overview of MDO 2:56:00

Overview of MDO Theory by Prof Joaquim R.R.A Martins, University of Michigan