episode-header-image

Apr 2024

1h 5m

Physics-Informed Neural Networks (PINNs)...

About this episode

Up next

Agentic AI for Engineers & How MATLAB & Simulink Workflows Are Changing - Seth DeLand | Podcast #171

🔗 Connect with Seth DeLand on LinkedIn: https://www.linkedin.com/in/seth-deland/In this episode, we sit down with Seth DeLand, Product Manager for Generative AI at MathWorks, to explore how agentic AI is transforming engineering workflows in 2026 and beyond.We discuss the evolut ... Show More

How Simulation Is Transforming Design & Development at ABM - Christian Bauernfeind | Podcast #170

🔗 Connect with Christian on LinkedIn: https://www.linkedin.com/in/christian-bauernfeind-b42532304/In this episode, we sit with Christian Bauernfeind, Head of Test Department at ABM, as he shares how ABM’s test department combines test benches and simulation to develop more effic ... Show More

Why Simulation Is Moving to SaaS and the Cloud - Joel Davison | Episode #169

🔗 More from Siemens: Multi-domain simulation - Unparalleled engineering excellence: https://blogs.sw.siemens.com/simcenter/multi-domain-simulation-simcenter-x/Riding the innovation bullet train: Simcenter X HPC: https://blogs.sw.siemens.com/simcenter/simcenter-x-hpc-aws-hpc7g/Si ... Show More

Recommended Episodes

[MINI] Recurrent Neural Networks

<p class="p1"><span class="s1">RNNs are a class of deep learning models designed to capture sequential behavior.<span class= "Apple-converted-space"> </span> An RNN trains a set of weights which depend not just on new input but also on the previous state of the neural network.<sp ... Show More

The Power of Graph Neural Networks: Understanding the Future of AI - Part 2/2 (Ep.224)

<p>In this episode of our podcast, we dive deep into the fascinating world of Graph Neural Networks.</p> <p>First, we explore Hierarchical Networks, which allow for the efficient representation and analysis of complex graph structures by breaking them down into smaller, more mana ... Show More

The Power of Graph Neural Networks: Understanding the Future of AI - Part 1/2 (Ep.223)

<p>In this episode, I explore the cutting-edge technology of graph neural networks (GNNs) and how they are revolutionizing the field of artificial intelligence. I break down the complex concepts behind GNNs and explain how they work by modeling the relationships between data poin ... Show More

Olaf Sporns on Network Neuroscience

The intersection between cutting-edge neuroscience and the emerging field of network science has been growing tremendously over the past decade. Olaf Sporns, editor of Network Neuroscience, and Distinguished Professor, Provost Professor of Department of Psychological and Brain Sc ... Show More

Why Deep Networks and Brains Learn Similar Features with Sophia Sanborn - #644

Today we’re joined by Sophia Sanborn, a postdoctoral scholar at the University of California, Santa Barbara. In our conversation with Sophia, we explore the concept of universality between neural representations and deep neural networks, and how these principles of efficiency pro ... Show More

SE Radio 594: Sean Moriarity on Deep Learning with Elixir and Axon

<p><strong>Sean Moriarity</strong>, creator of the Axon deep learning framework, co-creator of the Nx library, and author of <em>Machine Learning in Elixir</em> and <em>Genetic Algorithms in Elixir,</em> published by the Pragmatic Bookshelf, speaks with SE Radio host <a href= "ht ... Show More

The Complexity of Learning Neural Networks

Over the past several years, we have seen many success stories in machine learning brought about by deep learning techniques. While the practical success of deep learning has been phenomenal, the formal guarantees have been lacking. Our current theoretical understanding of the ma ... Show More

Easily Fooling Deep Neural Networks