MIRELAI: Machine Learning Workshop Unveils

We’re thrilled to announce the MIRELAI Machine Learning Workshop, organised by Polymer Competence Center Leoben GmbH (PCCL) Austria, an immersive two-day event poised to redefine the landscape of machine learning. Set to unfold on March 12-13, 2024 with our world class experts, this virtual workshop on Teams promises a deep dive into the latest breakthroughs and applications in the dynamic world of AI.

Curated with precision, this workshop brings together a lineup of industry experts, each a luminary in their respective fields. DCs will explore the cutting edge of:

  • Deep Learning for Composite Properties: Stoyan Stoyanov (UOG) will unveil the secrets behind deep learning models for Printed Circuit Board and its conductive layers
  • Computer Vision’s Machine Learning Makeover: Roberta Corti (Technoprobe) will cover the magic of machine learning applied to computer vision.
  • Hands-On PyTorch Classification: DCs will also dive into the world of PyTorch with Angelika Hable & Dieter P. Gruber (PCCL) as they guide you through a hands-on classification example.
  • Predictive Power of AI in Solder Strain: Bart Vandervelde (IMEC) will demonstrate the prowess of Artificial Neural Networks in predicting Ball Grid Array solder strain.
  • Uncertainties in ML models: Manfred Mücke (MCL) will take us through accounting for target quantity uncertainties in machine leaning models.
  • Beyond Predictions: Real-world Reinforcement Learning: Simon Hirländer (IDA Lab) explores the fascinating realm of reinforcement learning and its applications in real-world scenarios.
  • Physics-Informed Neural Networks (PINNs): Mathias Verbeke (KU Leuven) unravels the intersection of physics and neural networks, showcasing PINNs.
  • Industrial Insights into Simulation and Digital Twin: Onur Atak & Gwendal Jouan (SISW) provide an industrial perspective on machine learning’s role in simulation and digital twin technologies.

Interested to know more? Follow our social media channels for live updates!