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University of Wuppertal

Academic institutioneurope · de
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Research library34linked papers
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Selected work

Representative Papers

Walk-on-Spheres Monte Carlo and deep neural network approximations of elliptic PDEs with drift and killing

Aug 10, 2026

This work proposes an efficient method for solving linear elliptic partial differential equations with constant diffusion, drift, and killing terms by integrating an enhanced Walk-on-Spheres (WoS) Monte Carlo algorithm with deep neural networks. The approach constructs unbiased estimators through explicit stochastic time sampling and employs a tailored neural network architecture to approximate both the stochastic representation of the solution and the boundary data. It represents the first integration of stochastic representations for elliptic PDEs with drift and killing terms into a deep learning framework. The study establishes uniform error bounds for the Monte Carlo estimator and proves that the solution approximation achieves polynomial complexity in both accuracy and dimensionality, thereby significantly extending the theoretical foundations and practical applicability of numerical methods for high-dimensional PDEs.

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Representing the Non-dominated Set of Multi-objective Network Problems by Supported Non-dominated Points

Jul 13, 2026

This work addresses the challenge of efficiently generating high-quality fixed-size representations from the typically vast set of nondominated solutions in multi-objective network optimization. The authors propose using supported nondominated points—particularly extreme points—as a compact candidate set to replace the full nondominated set for subset selection. For the first time, they systematically demonstrate that supported nondominated points in capacitated network problems offer both high representational quality and computational efficiency. Experimental results show that fixed-size solution sets selected solely from this reduced candidate set achieve solution quality nearly equivalent to those selected from the complete nondominated set, while substantially reducing computational overhead.

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ConTex: Reformulating Counterfactual Generation For Time Series Forecasting

Jun 16, 2026

Existing time series forecasting models struggle to provide actionable intervention recommendations, while conventional counterfactual approaches suffer from instance inconsistency, high computational cost, and poor suitability for real-time deployment. This work proposes a model-agnostic, decomposed architecture that reframes counterfactual generation as the learning of globally consistent intervention policies, enabling efficient and stable counterfactual reasoning through shared functions. The method uniquely supports cross-instance consistent, sparse, and interpretable joint interventions over both time and features, employing a dual-head encoder to separately capture temporal dependencies and modification intensities of interventions. Experiments demonstrate that the proposed approach achieves state-of-the-art performance across multiple benchmarks, significantly improves intervention sparsity, reduces computational overhead by 12–36×, and requires only approximately 0.007 seconds per inference.

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Recent publications

Latest Papers

Walk-on-Spheres Monte Carlo and deep neural network approximations of elliptic PDEs with drift and killing

Aug 10, 2026

This work proposes an efficient method for solving linear elliptic partial differential equations with constant diffusion, drift, and killing terms by integrating an enhanced Walk-on-Spheres (WoS) Monte Carlo algorithm with deep neural networks. The approach constructs unbiased estimators through explicit stochastic time sampling and employs a tailored neural network architecture to approximate both the stochastic representation of the solution and the boundary data. It represents the first integration of stochastic representations for elliptic PDEs with drift and killing terms into a deep learning framework. The study establishes uniform error bounds for the Monte Carlo estimator and proves that the solution approximation achieves polynomial complexity in both accuracy and dimensionality, thereby significantly extending the theoretical foundations and practical applicability of numerical methods for high-dimensional PDEs.

0 citationsRead paper

Representing the Non-dominated Set of Multi-objective Network Problems by Supported Non-dominated Points

Jul 13, 2026

This work addresses the challenge of efficiently generating high-quality fixed-size representations from the typically vast set of nondominated solutions in multi-objective network optimization. The authors propose using supported nondominated points—particularly extreme points—as a compact candidate set to replace the full nondominated set for subset selection. For the first time, they systematically demonstrate that supported nondominated points in capacitated network problems offer both high representational quality and computational efficiency. Experimental results show that fixed-size solution sets selected solely from this reduced candidate set achieve solution quality nearly equivalent to those selected from the complete nondominated set, while substantially reducing computational overhead.

0 citationsRead paper

ConTex: Reformulating Counterfactual Generation For Time Series Forecasting

Jun 16, 2026

Existing time series forecasting models struggle to provide actionable intervention recommendations, while conventional counterfactual approaches suffer from instance inconsistency, high computational cost, and poor suitability for real-time deployment. This work proposes a model-agnostic, decomposed architecture that reframes counterfactual generation as the learning of globally consistent intervention policies, enabling efficient and stable counterfactual reasoning through shared functions. The method uniquely supports cross-instance consistent, sparse, and interpretable joint interventions over both time and features, employing a dual-head encoder to separately capture temporal dependencies and modification intensities of interventions. Experiments demonstrate that the proposed approach achieves state-of-the-art performance across multiple benchmarks, significantly improves intervention sparsity, reduces computational overhead by 12–36×, and requires only approximately 0.007 seconds per inference.

0 citationsRead paper