research
∙
08/10/2023
Rethinking Integration of Prediction and Planning in Deep Learning-Based Automated Driving Systems: A Review
Automated driving has the potential to revolutionize personal, public, a...
share
research
∙
06/22/2023
Uncertainty Propagation of Initial Conditions in Thermal Models
The operation of machine tools often demands a highly accurate knowledge...
research
∙
06/01/2023
Stay on Track: A Frenet Wrapper to Overcome Off-road Trajectories in Vehicle Motion Prediction
Predicting the future motion of observed vehicles is a crucial enabler f...
research
∙
05/30/2023
Scaling Planning for Automated Driving using Simplistic Synthetic Data
We challenge the perceived consensus that the application of deep learni...
research
∙
03/16/2023
Adaptive rational Krylov methods for exponential Runge–Kutta integrators
We consider the solution of large stiff systems of ordinary differential...
research
∙
02/16/2023
A weighted subspace exponential kernel for support tensor machines
High-dimensional data in the form of tensors are challenging for kernel ...
research
∙
02/15/2023
From Prediction to Planning With Goal Conditioned Lane Graph Traversals
The field of motion prediction for automated driving has seen tremendous...
research
∙
12/02/2022
Gibbs-Helmholtz Graph Neural Network: capturing the temperature dependency of activity coefficients at infinite dilution
The accurate prediction of physicochemical properties of chemical compou...
research
∙
05/15/2022
A comparison of PINN approaches for drift-diffusion equations on metric graphs
In this paper we focus on comparing machine learning approaches for quan...
research
∙
04/07/2022
Preconditioning for a Phase-Field Model with Application to Morphology Evolution in Organic Semiconductors
The Cahn–Hilliard equations are a versatile model for describing the evo...
research
∙
11/19/2021
Learning in High-Dimensional Feature Spaces Using ANOVA-Based Fast Matrix-Vector Multiplication
Kernel matrices are crucial in many learning tasks such as support vecto...
research
∙
10/07/2021
An Improved Penalty Algorithm using Model Order Reduction for MIPDECO problems with partial observations
This work addresses optimal control problems governed by a linear time-d...
research
∙
09/15/2021
RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs
In recent years, graph neural networks (GNNs) have gained increasing pop...
research
∙
09/07/2021
A low-rank tensor method to reconstruct sparse initial states for PDEs with Isogeometric Analysis
When working with PDEs the reconstruction of a previous state often prov...
research
∙
07/27/2021
Orientations and matrix function-based centralities in multiplex network analysis of urban public transport
We study urban public transport systems by means of multiplex networks i...
research
∙
04/29/2021
Matrix function-based centrality measures for layer-coupled multiplex networks
Centrality measures identify the most important nodes in a complex netwo...
research
∙
04/16/2021
A Study of Graph-Based Approaches for Semi-Supervised Time Series Classification
Time series data play an important role in many applications and their a...
research
∙
08/03/2020
Pseudoinverse Graph Convolutional Networks: Fast Filters Tailored for Large Eigengaps of Dense Graphs and Hypergraphs
Graph Convolutional Networks (GCNs) have proven to be successful tools f...
research
∙
07/10/2020
Semi-supervised Learning for Multilayer Graphs Using Diffuse Interface Methods and Fast Matrix Vector Products
We generalize a graph-based multiclass semi-supervised classification te...
research
∙
05/29/2020
A low-rank matrix equation method for solving PDE-constrained optimization problems
PDE-constrained optimization problems arise in a broad number of applica...
research
∙
02/12/2020
Efficient Structure-preserving Support Tensor Train Machine
Deploying the multi-relational tensor structure of a high dimensional fe...
research
∙
12/17/2019
A literature survey of matrix methods for data science
Efficient numerical linear algebra is a core ingredient in many applicat...
research
∙
12/14/2019
Solving differential Riccati equations: A nonlinear space-time method using tensor trains
Differential algebraic Riccati equations are at the heart of many applic...
research
∙
07/17/2019
Optimization of a partial differential equation on a complex network
Differential equations on metric graphs can describe many phenomena in t...
research
∙
07/15/2019
Improved Penalty Algorithm for Mixed Integer PDE Constrained Optimization (MIPDECO) Problems
Optimal control problems including partial differential equation (PDE) a...
research
∙
05/24/2019
Semi-Supervised Classification on Non-Sparse Graphs Using Low-Rank Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have proven to be successful tools f...
research
∙
09/07/2018
Node classification for signed networks using diffuse interface methods
Signed networks are a crucial tool when modeling friend and foe relation...
research
∙
08/14/2018
NFFT meets Krylov methods: Fast matrix-vector products for the graph Laplacian of fully connected networks
The graph Laplacian is a standard tool in data science, machine learning...
research
∙
11/18/2016
