Search Results for author:
Found 18 papers, 6 papers with code
Unsupervised Representation Learning for Diverse Deformable Shape Collections
Unlike previous 3D mesh autoencoders that require meshes to be in a 1-to-1 correspondence, our approach is trained on diverse meshes in an unsupervised manner.
On minimizing the training set fill distance in machine learning regression
We empirically show that selecting a training set by aiming to minimize the fill distance, thereby minimizing our derived bound, significantly reduces the maximum prediction error of various regression models, outperforming alternative sampling approaches by a large margin.
On the Interplay of Subset Selection and Informed Graph Neural Networks
However, learning on large datasets is strongly limited by the availability of computational resources and can be infeasible in some scenarios.
Graph Extraction for Assisting Crash Simulation Data Analysis
In this work, we establish a method for abstracting information from Computer Aided Engineering (CAE) into graphs.
Evolutionary Solution Adaption for Multi-Objective Metal Cutting Process Optimization
Optimizing manufacturing process parameters is typically a multi-objective problem with often contradictory objectives such as production quality and production time.
Transfer Learning using Spectral Convolutional Autoencoders on Semi-Regular Surface Meshes
To study the deformation patterns of unseen shapes by transfer learning, we want to train an autoencoder that can analyze new surface meshes without training a new network.
Graph Modeling in Computer Assisted Automotive Development
We consider graph modeling for a knowledge graph for vehicle development, with a focus on crash safety.
Canonical convolutional neural networks
We introduce canonical weight normalization for convolutional neural networks.
The elements of flexibility for task-performing systems
We call these design features the "elements of flexibility".
Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes
The analysis of deforming 3D surface meshes is accelerated by autoencoders since the low-dimensional embeddings can be used to visualize underlying dynamics.
Multi-resolution Dynamic Mode Decomposition for Damage Detection in Wind Turbine Gearboxes
We introduce an approach for damage detection in gearboxes based on the analysis of sensor data with the multi-resolution dynamic mode decomposition (mrDMD).
Wavelet-Packets for Deepfake Image Analysis and Detection
Additionally, this paper proposes to learn a model for the detection of synthetic images based on the wavelet-packet representation of natural and GAN-generated images.
Analysis and Prediction of Deforming 3D Shapes using Oriented Bounding Boxes and LSTM Autoencoders
For sequences of complex 3D shapes in time we present a general approach to detect patterns for their analysis and to predict the deformation by making use of structural components of the complex shape.
A Compact Spectral Descriptor for Shape Deformations
The proposed descriptor provides a novel approach to the parametrization of geometric deformation behavior and enables the use of state-of-the-art data analysis techniques such as machine learning to engineering tasks concerned with plastic deformation behavior.
Unsupervised Learning of Automotive 3D Crash Simulations using LSTMs
Long short-term memory (LSTM) networks allow to exhibit temporal dynamic behavior with feedback connections and seem a natural choice for learning sequences of 3D meshes.
Explainable Machine Learning for Scientific Insights and Discoveries
Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data.
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems
It considers the source of knowledge, its representation, and its integration into the machine learning pipeline.
Event-triggered Learning for Resource-efficient Networked Control
Common event-triggered state estimation (ETSE) algorithms save communication in networked control systems by predicting agents' behavior, and transmitting updates only when the predictions deviate significantly.
