ECLIPSE Lab — Presentations & Teaching

Lecture slides, conference talks, and course materials from the ECLIPSE Lab at FAU Erlangen-Nürnberg.

Data Science for Electron Microscopy

Unit 01
Intro
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Unit 02
Regression
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Unit 03
CNNs
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Unit 04
Self-Supervised Learning
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Unit 05
GANs
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Unit 06
Gaussian Processes
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Unit 07
Gaussian Processes II
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Unit 08
Imaging Inverse Problems I
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Unit 09
Imaging Inverse Problems II
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Mathematical Foundations of AI & ML

Unit 01
Learning vs Data Analysis
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Unit 02
Linear Algebra, PCA and SVD
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Unit 03
Regression as Loss Minimization
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Unit 04
Neural Networks and Activations
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Unit 05
Backpropagation and Gradient Flow
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Unit 06
Loss Landscapes and Optimization
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Unit 07
Generalization and Bias-Variance
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Unit 08
Probabilistic View of Learning
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Unit 09
Representation Learning
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Unit 10
Latent Spaces and Embeddings
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Unit 11
Unsupervised Learning
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Unit 12
Uncertainty in Predictions
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Unit 13
Physics Informed Learning
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Unit 14
Explainability, Limits, and Trust
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Materials Genomics

Unit 01
Quantum Mechanics and Quantum Chemistry
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Unit 02
Simulation Methods as Data Generators
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Unit 03
Atomistic and Electronic Simulations
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Unit 04
Continuum Simulations, Thermodynamics, and Stability
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Unit 05
Graph-Based Crystal Representations
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Unit 06
Local Atomic Environments
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Unit 07
Regression and Generalization in Materials Data
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Unit 08
Neural Networks for Materials Properties
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Unit 09
Representation Learning and Feature Discovery
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Unit 10
Latent Spaces of Materials
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Unit 11
Clustering vs Discovery in Materials Spaces
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Unit 12
Uncertainty-Aware Discovery & Gaussian Processes
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Unit 13
Physical Constraints, Trust, and Integration Outlook
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Machine Learning for Characterization and Processing

Unit 01
What makes materials data special?
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Unit 02
Physics of data formation
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Unit 03
Data quality, labels, and leakage
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Unit 04
From classical microstructure metrics to learned representations
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Unit 05
Neural networks for microstructure images
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Unit 06
Data scarcity & transfer learning
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Unit 07
Time-series and process monitoring
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Unit 08
Generalization, robustness, and process windows
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Unit 09
Inverse problems and process maps
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Unit 10
ML for characterization signals
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Unit 11
Automation in microscopy and characterization
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Unit 12
Uncertainty-aware regression & Gaussian Processes
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Unit 13
Physics-informed and constrained ML
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Unit 14
Integration, limits, and reflection
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Conference Talks

Talk
2025 MC
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Talk
2025 M&M
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