Lectures related to Computational engineering

Data-Driven Modeling in Neuroscience: Meenakshi Khosla

By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.

Master's Program in Computational Science and Engineering

Outlines the Master's program in Computational Science and Engineering at EPFL, detailing its structure, curriculum, and career opportunities for graduates.

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Scientific Computing in Neuroscience

Explores scientific computing in neuroscience, emphasizing the simulation of neurons and networks using tools like NEURON, NEST, and BRIAN.

Computational Science and Engineering: Master Program Overview

Outlines the Master in Computational Science and Engineering program at EPFL, detailing its structure, projects, and career opportunities for graduates.

Digital Humanities Workflow

Explores the workflow in Digital Humanities, emphasizing data collection and visualization.

Introduction to NumPy and Matplotlib for Scientific Computing

Introduces NumPy and Matplotlib, essential tools for scientific computing and data visualization in Python.

Advanced Analysis II: Differential Equations and Timers

Discusses advanced analysis concepts, focusing on differential equations and timers in microcontrollers.

Efficient Stochastic Numerical Methods

Explores efficient stochastic numerical methods for modeling and learning, covering topics like the Analytical Engine and kinase inhibitors.

Introduction to NumPy: Basics of Scientific Computing

Introduces NumPy, focusing on array creation, manipulation, and its advantages for scientific computing.

Residue Theorem: Applications in Complex Analysis

Discusses the residue theorem and its applications in calculating complex integrals.

Taylor Series and Secant Method: Numerical Analysis Techniques

Discusses the Taylor series and secant method, focusing on their applications in numerical analysis and root-finding techniques.

Finite Differences and Finite Elements: Variational Formulation

Discusses finite differences and finite elements, focusing on variational formulation and numerical methods in engineering applications.

Singular Value Decomposition: Image Compression and Applications

Covers Singular Value Decomposition, focusing on its application in image compression and data representation.

Path Integral Methods: Advanced Techniques

Explores advanced path integral methods in computational science, covering efficient sampling, colored noise, high-order integrals, and quantum thermostats.

Feedback Mechanisms in Visual Intelligence and Adaptation

Explores the significance of feedback mechanisms in visual intelligence and adaptation processes.

Root Finding Methods: Bisection and Secant Techniques

Covers root-finding methods, focusing on the bisection and secant techniques, their implementations, and comparisons of their convergence rates.

Numerical Methods: Bisection and Multidimensional Arrays

Discusses the bisection method for solving nonlinear equations and its implementation using Python with NumPy and Matplotlib.

Molecular Sciences: Software Solutions

Explores software solutions for molecular sciences, covering cell-cell signaling, macromolecular dynamics, and machine learning.

Lightcone Conformal Truncation and S-matrix Bootstrap

Delves into lightcone conformal truncation and the S-matrix bootstrap method for quantum field theory computations.