Dr. Andreas Wiegmann
From Filter Media Images to Performance Prediction: Combining AI and Physics-Based Simulation to design next generation filter media
CEO
Math2Market GmbH, Germany
Speaker Bio
Dr. Andreas Wiegmann is CEO of Math2Market GmbH in Kaiserslautern, Germany, and President of Math2Market North America, Inc. He holds a Diploma in Mathematics with a minor in Computer Science from the Technical University of Karlsruhe (now KIT) and a PhD in Mathematics from the University of Washington, Seattle. USA. After postdoctoral research at UC Berkeley and Lawrence Berkeley National Laboratory, he joined the German Fraunhofer Institute for Industrial Mathematics (ITWM) in 1999.
His work on random 3D material models and image-based material property computations lead to the creation of the GeoDict software platform in 2001 and the founding of Math2Market in 2011. Today, GeoDict is used by more than 500 renowned companies and research institutions worldwide for digital material R&D. Andreas has authored more than 180 peer reviewed publications, with around 8,200 citations.
Presentation time
December 9, 2026
10:40 AM to 11:50 AM EST
Abstract
Developing high-performance filter media requires understanding how their complex
microstructure affects pressure drop, filtration efficiency, particle deposition, and filter lifetime.
Artificial intelligence (AI) can support this process by extracting quantitative information from
experimental image data and making it available for digital material development.
This presentation shows how AI and physics-based simulation can be combined to support filtration
R&D from microstructure characterization to performance prediction. AI-assisted image processing
and segmentation transform micro-CT and other 3D image data into digital representations of real
filter media. For fibrous materials, neural networks can identify individual fibers and determine
characteristics such as fiber diameter, orientation, length, and curvature. This data can also serve
as a basis to create representative digital models.
Physics-based simulations then connect these resulting structures to filtration performance.
Properties such as permeability, pressure drop, filter efficiency, particle deposition, clogging
behavior, and filter capacity can be predicted for existing or virtually modified media. Structural
parameters such as fiber diameter, orientation, porosity, gradients, thickness, and pleat geometry
can then be varied in the model systematically.
This combination of AI-based characterization, digital modeling, and simulation supports the
efficient exploration of new filter designs with a focus on meeting performance requirements