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Machine learning pipelines that include a combinatorial optimization layer can give surprisingly efficient heuristics for difficult combinatorial optimization problems. Three questions remain open: which architecture should be used, how…

Robotics · Computer Science 2023-02-07 Axel Parmentier

The ever-increasing parameter counts of deep learning models necessitate effective compression techniques for deployment on resource-constrained devices. This paper explores the application of information geometry, the study of…

Machine Learning · Computer Science 2025-07-15 Zakhar Shumaylov , Vasileios Tsiaras , Yannis Stylianou

Highly-optimized complex transport networks serve crucial functions in many man-made and natural systems such as power grids and plant or animal vasculature. Often, the relevant optimization functional is non-convex and characterized by…

Biological Physics · Physics 2016-09-23 Henrik Ronellenfitsch , Eleni Katifori

We consider the numerical approximation of compressible flow in a pipe network. Appropriate coupling conditions are formulated that allow us to derive a variational characterization of solutions and to prove global balance laws for the…

Numerical Analysis · Mathematics 2016-10-06 Herbert Egger

Approximate dynamic programming has been investigated and used as a method to approximately solve optimal regulation problems. However, the extension of this technique to optimal tracking problems for continuous time nonlinear systems has…

Systems and Control · Computer Science 2017-07-25 Rushikesh Kamalapurkar , Huyen Dinh , Shubhendu Bhasin , Warren Dixon

Optimization is widely used to determine the physical and financial exchange of wholesale electricity in organized markets. Guarantees of solution optimality and feasibility rest largely on convexity, which is not in general a…

Optimization and Control · Mathematics 2026-05-22 Sidhant Misra , Marc Vuffray , Anatoly Zlotnik , Aleksandr M. Rudkevich

Optimal transportation provides a means of lifting distances between points on a geometric domain to distances between signals over the domain, expressed as probability distributions. On a graph, transportation problems can be used to…

Optimization and Control · Mathematics 2018-03-26 Montacer Essid , Justin Solomon

This article presents a new and efficient alternative to well established algorithms for molecular geometry optimization. The new approach exploits the approximate decoupling of molecular energetics in a curvilinear internal coordinate…

Materials Science · Physics 2007-05-23 Károly Németh , Matt Challacombe

Fibrous networks are ideal functional materials since they provide mechanical rigidity at low weight. Such structures are omnipresent in natural biomaterials from cells to tissues, as well as in man-made materials from polymeric composites…

Liquid dropout and retention in gas-condensate reservoirs, specially in the near wellbore region, obstruct gas flowing paths and impact negatively the produced fluid volume and composition. Yet, condensate banking forecasting is commonly…

Fluid Dynamics · Physics 2020-11-18 Paula Reis , Marcio Carvalho

Designing and analyzing algorithms with provable performance guarantees enables efficient optimization problem solving in different application domains, e.g.\ communication networks, transportation, economics, and manufacturing. Despite the…

Optimization and Control · Mathematics 2019-09-30 Dimitrios Letsios , Radu Baltean-Lugojan , Francesco Ceccon , Miten Mistry , Johannes Wiebe , Ruth Misener

Modern FPGAs continue to increase in capacity which requires more memory to run the CAD flow. The routing resource graph, which is needed by the detailed router, is a memory hungry data structure which describes all of the physical…

Distributed, Parallel, and Cluster Computing · Computer Science 2018-11-13 Andrew Kennings

In dynamical systems reconstruction (DSR) we seek to infer from time series measurements a generative model of the underlying dynamical process. This is a prime objective in any scientific discipline, where we are particularly interested in…

Machine Learning · Computer Science 2024-06-10 Christoph Jürgen Hemmer , Manuel Brenner , Florian Hess , Daniel Durstewitz

Many industrial applications rely on real-time optimization to improve key performance indicators. In the case of unknown process characteristics, real-time optimization becomes challenging, particularly for the satisfaction of safety…

Optimization and Control · Mathematics 2023-02-03 Buse Sibel Korkmaz , Marta Zagórowska , Mehmet Mercangöz

We present Flow-Induced Diagonal Gaussian Processes (FiD-GP), a compression framework that incorporates a compact inducing weight matrix to project a neural network's weight uncertainty into a lower-dimensional subspace. Critically, FiD-GP…

Machine Learning · Computer Science 2025-10-06 Moule Lin , Andrea Patane , Weipeng Jing , Shuhao Guan , Goetz Botterweck

The minimization of operation costs for natural gas transport networks is studied. Based on a recently developed model hierarchy ranging from detailed models of instationary partial differential equations with temperature dependence to…

Optimization and Control · Mathematics 2017-12-08 Volker Mehrmann , Martin Schmidt , Jeroen J. Stolwijk

This paper describes the project GivEn that develops a novel multicriteria optimization process for gas turbine blades and vanes using modern "adjoint" shape optimization algorithms. Given the many start and shut-down processes of gas power…

We combine welding process modelling with deformation-diffusion-fracture (embrittlement) simulations to predict failures in hydrogen transport pipelines. The focus is on the structural integrity of seam welds, as these are often the…

Computational Engineering, Finance, and Science · Computer Science 2024-01-23 T. K. Mandal , J. Parker , M. Gagliano , E. Martínez-Pañeda

The flow of gas through networks of pipes can be modeled by coupling hyperbolic systems of partial differential equations that describe the flow through the pipes that form the edges of the graph of the network by algebraic node conditions…

Analysis of PDEs · Mathematics 2023-03-01 Martin Gugat , Jan Giesselmann , Teresa Kunkel

Topology Optimization seeks to find the best design that satisfies a set of constraints while maximizing system performance. Traditional iterative optimization methods like SIMP can be computationally expensive and get stuck in local…

Machine Learning · Computer Science 2023-03-20 Giorgio Giannone , Faez Ahmed