Results 91 to 100 of about 2,048 (231)

Physics-constrained convolutional neural networks for inverse problems in spatiotemporal partial differential equations

Data-Centric Engineering
We propose a physics-constrained convolutional neural network (PC-CNN) to solve two types of inverse problems in partial differential equations (PDEs), which are nonlinear and vary both in space and time.
Daniel Kelshaw, Luca Magri
doaj   +1 more source

Equilibrium Reward for Liquidity Providers in Automated Market Makers

Mathematical Finance, EarlyView.
ABSTRACT We find the equilibrium contract that an automated market maker (AMM) offers to their strategic liquidity providers (LPs) in order to maximize the order flow that gets processed by the venue. Our model is formulated as a leader–follower stochastic game, where the venue is the leader and a representative LP is the follower.
Alif Aqsha, Philippe Bergault, Leandro Sánchez‐Betancourt   +2 more
wiley   +1 more source

Deep Neural Networks motivated by PDEs

, 2020
One of the most promising areas in artificial intelligence is deep learning, a form of machine learning that uses neural networks containing many hidden layers. Recent success has led to breakthroughs in applications such as speech and image recognition.
Ruthotto, Lars
core  

Anisotropic variational models and PDEs for inverse imaging problems

, 2019
In this thesis we study new anisotropic variational regularisers and partial differential equations (PDEs) for solving inverse imaging problems that arise in a variety of real-world applications. Firstly, we introduce a new anisotropic higher-order total

core   +1 more source

Random Carbon Tax Policy and Investment Into Emission Abatement Technologies

Mathematical Finance, EarlyView.
ABSTRACT We analyze the problem of a profit‐maximizing electricity producer, subject to carbon taxes, who decides on investments into CO2$\rm CO_2$ abatement technologies. We assume that the carbon tax policy is random and that the investment in the abatement technology is divisible, irreversible, and subject to transaction costs.
Katia Colaneri, Rüdiger Frey, Verena Köck   +2 more
wiley   +1 more source

Particle methods for Bayesian inverse problems governed by partial differential equations (PDEs)

, 2022
Inverse problems enable integration of observational and experimental data, simulations and/or mathematical models to make scientific predictions. Solving an inverse problem with the Bayesian framework requires exploring a high dimensional, non-Gaussian ...
Myers, Aaron Noah
core   +1 more source

BiLO: Bilevel Local Operator Learning for PDE Inverse Problems

Journal of Computational Physics
Abstract We propose a new neural network based method for solving inverse problems for partial differential equations (PDEs) by formulating the PDE inverse problem as a bilevel optimization problem. At the upper level, we minimize the data loss with respect to the PDE parameters.
Ray Zirui Zhang, Christopher E. Miles, Xiaohui Xie, John S. Lowengrub   +3 more
openaire   +2 more sources

Relative Arbitrage Opportunities With Interactions Among N Investors

Mathematical Finance, EarlyView.
ABSTRACT The relative arbitrage portfolio outperforms a benchmark portfolio over a given time‐horizon with probability one. With market price of risk processes depending on the market portfolio and investors, this paper analyzes the multi‐agent optimization of relative arbitrage opportunities in the coupled system of market and wealth dynamics.
Tomoyuki Ichiba, Nicole Tianjiao Yang
wiley   +1 more source

A Model of Strategic Sustainable Investment

Mathematical Finance, EarlyView.
ABSTRACT We study a problem of optimal irreversible investment and emission reduction formulated as a nonzero‐sum dynamic game between an investor with environmental preferences and a firm. The game is set in continuous‐time on an infinite‐time horizon.
Tiziano De Angelis, Caio César Graciani Rodrigues, Peter Tankov   +2 more
wiley   +1 more source

Invariant Measure and Universality of the 2D Yang–Mills Langevin Dynamic

Communications on Pure and Applied Mathematics, Volume 79, Issue 8, Page 1973-2102, August 2026.
ABSTRACT We prove that the Yang–Mills (YM) measure for the trivial principal bundle over the two‐dimensional torus, with any connected, compact structure group, is invariant for the associated renormalised Langevin dynamic. Our argument relies on a combination of regularity structures, lattice gauge‐fixing and Bourgain's method for invariant measures ...
Ilya Chevyrev, Hao Shen
wiley   +1 more source