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; Models, Loss Functions
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Unit 02
Linear Algebra Refresher; Covariance, PCA/SVD
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Unit 03
Regression as Loss Minimization
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Unit 04
Neural Networks — From Neurons to CNNs
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Unit 05
Clustering & Autoencoders
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Unit 06
Loss Landscapes & Optimization Behavior
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Unit 07
Probabilistic View of Learning; Noise; Conformal Prediction
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Unit 08
Generalization, Bias-Variance, Regularization, Tree Ensembles
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Unit 09
Latent Spaces & Advanced Representation Learning
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Unit 10
Attention & Transformers
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Unit 11
Generative Models — VAE & Diffusion
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Unit 12
Uncertainty in Predictions
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Unit 13
Physics-Informed & Constrained Learning
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Unit 14
Explainability, Limits, and Scientific Trust
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Materials Genomics

Unit 01
What is Materials Genomics?
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Unit 02
QM Postulates, Solvable Systems, Multi-Electron Atoms
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Unit 03
Quantum Chemistry Methods (HF, MP, CC, DFT)
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Unit 04
Thermodynamics, Statistical Mechanics & Classical Atomistic Simulation
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Unit 05
Monte Carlo Sampling & Continuum Mechanics
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Unit 06
Local Atomic Environments & Universal MLIPs
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Unit 07
Graph-Based Crystal Representations
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Unit 08
Regression and Generalization in Materials Data
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Unit 09
Neural Networks for Materials Properties
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Unit 10
Representation Learning and Feature Discovery
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Unit 12
Generative Models & Inverse Design
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Unit 13
Uncertainty-Aware Discovery & Gaussian Processes
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Unit 14
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
Unsupervised methods for materials — clustering & autoencoders
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Unit 06
Data scarcity & transfer learning
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Unit 07
Generalization, robustness, and process windows
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Unit 08
Inverse problems and process maps
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Unit 09
ML for characterization signals
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Unit 09b
Transformers for materials (ViT, Flash Attention, Mamba)
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Unit 10
Automation in microscopy and characterization
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Unit 11
Uncertainty-aware regression & Gaussian Processes
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Unit 12
Physics-informed and constrained ML
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Unit 13
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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