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We formulate the immersed-boundary method (IBM) as an inverse problem. A control variable is introduced on the boundary of a larger domain that encompasses the target domain. The optimal control is the one that minimizes the mismatch…

Optimization and Control · Mathematics 2019-09-04 Jianfeng Yan , Jason Edward Hicken

Accurate traffic flow estimation and prediction are critical for the efficient management of transportation systems, particularly under increasing urbanization. Traditional methods relying on static sensors often suffer from limited spatial…

Machine Learning · Computer Science 2025-03-19 Jake Rap , Amritam Das

This work is devoted to the theoretical and numerical analysis of a two-species chemotaxis- Navier-Stokes system with Lotka-Volterra competitive kinetics in a bounded domain of Rd, d = 2, 3. First, we study the existence of global weak…

We introduce the proximal optimal transport divergence, a novel discrepancy measure that interpolates between information divergences and optimal transport distances via an infimal convolution formulation. This divergence provides a…

Optimization and Control · Mathematics 2025-08-11 Ricardo Baptista , Panagiota Birmpa , Markos A. Katsoulakis , Luc Rey-Bellet , Benjamin J. Zhang

We study the worst-case convergence rates of the proximal gradient method for minimizing the sum of a smooth strongly convex function and a non-smooth convex function whose proximal operator is available. We establish the exact worst-case…

Optimization and Control · Mathematics 2020-03-03 Adrien B. Taylor , Julien M. Hendrickx , François Glineur

Connecting optimal transport and variational inference, we present a principled and systematic framework for sampling and generative modelling centred around divergences on path space. Our work culminates in the development of the…

Machine Learning · Statistics 2025-05-08 Francisco Vargas , Shreyas Padhy , Denis Blessing , Nikolas Nüsken

High-fidelity modeling of turbulent flows requires capturing complex spatiotemporal dynamics and multi-scale intermittency, posing a fundamental challenge for traditional knowledge-based systems. While deep generative models, such as…

Machine Learning · Computer Science 2026-04-08 Li Kunpeng , Wan Chenguang , Qu Zhisong , Lim Kyungtak , Virginie Grandgirard , Xavier Garbet , Yu Hua , Ong Yew Soon

The Ordered Upwind Method (OUM) is used to approximate the viscosity solution of the static Hamilton-Jacobi-Bellman (HJB) with direction-dependent weights on unstructured meshes. The method has been previously shown to provide a solution…

Optimization and Control · Mathematics 2016-01-13 Alex Shum , Kirsten Morris , Amir Khajepour

Following [21, 23], the present work investigates a new relative entropy-regularized algorithm for solving the optimal transport on a graph problem within the randomized shortest paths formalism. More precisely, a unit flow is injected into…

Machine Learning · Computer Science 2021-09-21 Sylvain Courtain , Guillaume Guex , Ilkka Kivimaki , Marco Saerens

The construction of weak solutions to compressible Navier-Stokes equations via a numerical method (including a rigorous proof of the convergence) is in a short supply, and so far, available only for one sole numerical scheme suggested in…

Numerical Analysis · Mathematics 2020-07-06 Young-Sam Kwon , Antonin Novotny

Although deep neural networks are well-known for their outstanding performance in tackling complex tasks, their hunger for computational resources remains a significant hurdle, posing energy-consumption issues and restricting their…

Machine Learning · Computer Science 2025-07-16 Victor Quétu , Zhu Liao , Nour Hezbri , Fabio Pizzati , Enzo Tartaglione

A framework of finite-velocity model based Boltzmann equation has been developed for convection-diffusion equations. These velocities are kept flexible and adjusted to control numerical diffusion. A flux difference splitting based kinetic…

Numerical Analysis · Mathematics 2024-10-01 S. V. Raghurama Rao , K. S. Shrinath , Ankit Ruhi , Veeredhi Vasudeva Rao

We study a mathematical model of a compressible viscous fluid driven by stochastic forces under slip boundary conditions of friction type. We introduce a notion of a weak solution that is analytically and probabilistically consistent with…

Probability · Mathematics 2026-01-23 Reo Tsuboya

We propose a semi-discrete numerical scheme and establish well-posedness of a class of parabolic systems. Such systems naturally arise while studying the optimal control of grain boundary motions. The latter is typically described using a…

Analysis of PDEs · Mathematics 2018-10-26 Harbir Antil , Ken Shirakawa , Noriaki Yamazaki

A robust drift-safe rendezvous trajectory optimization tool is developed in this work, with applications to orbital rendezvous and proximity operations. The method is based on direct collocation and utilizes a sequential convex programming…

Systems and Control · Electrical Eng. & Systems 2024-01-23 Andrew W. Berning , Ethan R. Burnett , Stefan Bieniawski

We present a novel framework inspired by the Immersed Boundary Method for predicting the fluid-structure interaction of complex structures immersed in flows with moderate to high Reynolds numbers. The main novelties of the proposed…

In this work, a higher order compact (HOC) discretization is developed on the nonuniform polar grid. The discretization conceptualized using the unsteady convection-diffusion equation (CDE) is further extended to flow problems governed by…

Fluid Dynamics · Physics 2024-09-27 Dharmaraj Deka , Shuvam Sen

We construct high-order semi-discrete-in-time and fully discrete (with Fourier-Galerkin in space) schemes for the incompressible Navier-Stokes equations with periodic boundary conditions, and carry out corresponding error analysis. The…

Numerical Analysis · Mathematics 2021-03-23 Fukeng Huang , Jie Shen

Existing Vision-Language models (VLMs) estimate either long-term trajectory waypoints or a set of control actions as a reactive solution for closed-loop planning based on their rich scene comprehension. However, these estimations are coarse…

Robotics · Computer Science 2024-04-01 Pranjal Paul , Anant Garg , Tushar Choudhary , Arun Kumar Singh , K. Madhava Krishna

We investigate the optimal transport problem between probability measures when the underlying cost function is understood to satisfy a least action principle, also known as a Lagrangian cost. These generalizations are useful when connecting…

Machine Learning · Computer Science 2024-06-04 Aram-Alexandre Pooladian , Carles Domingo-Enrich , Ricky T. Q. Chen , Brandon Amos