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The impact of cosmic ray feedback during the epoch of reionisation

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arXiv 2025年

作者： Farcy, Marion Rosdahl, Joakim Dubois, Yohan Blaizot, Jérémy Martin-Alvarez, Sergio Haehnelt, Martin Kimm, Taysun Teyssier, Romain Institute for Physics Laboratory for Galaxy Evolution and Spectral Modelling EPFL Observatoire de Sauverny Chemin Pegasi 51 Versoix1290 Switzerland Centre de Recherche Astrophysique de Lyon CNRS UMR 5574 Univ. Lyon Ens de Lyon 9 avenue Charles André Saint-Genis-LavalF-69230 France Institut d’Astrophysique de Paris CNRS UMR 7095 Sorbonne Université 98 bis bd Arago Paris75014 France Stanford University StanfordCA94305 United States Kavli Institute for Cosmology Institute of Astronomy Madingley Road CambridgeCB3 0HA United Kingdom Department of Astronomy Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul03722 Korea Republic of Department of Astrophysical Sciences Princeton University Peyton Hall PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University Fine Hall Washington Road PrincetonNJ08544-1000 United States

Galaxies form and evolve via a multitude of complex physics. In this work, we investigate the role of cosmic ray (CR) feedback in galaxy evolution and reionisation, by examining its impact on the escape of ionising radiation from galaxies. For this purpose, we present two sphinx cosmological radiation-magneto-hydrodynamics simulations, allowing for the first time a study of the impact of CR feedback on thousands of resolved galaxies during the Epoch of Reionisation (EoR). The simulations differ in their feedback prescriptions: one adopts a calibrated strong supernova (SN) feedback, while the other simulation reduces the strength of SN feedback and includes CR feedback instead. We show that both comparably regulate star formation, reasonably match observations of high-redshift UV luminosity functions, and produce a similar amount of hydrogen ionising photons. In contrast to the model with strong SN feedback, the model with CRs lead to incomplete reionisation, which is in strong disagreement with observational estimates of the reionisation history. This is due to CR feedback shaping the ISM differently, filling with gas the low density cavities otherwise carved by SN explosions. As a result, this reduces the escape of ionising photons, at any halo mass, and primarily in the close vicinity of the stars. Our study indicates that CR feedback regulates galaxy growth during the EoR, but negatively affects reionisation, a tension which paves the way for further exploration and refinement of existing galaxy formation and feedback models. Such improvements are crucial in order to capture and understand the process of reionisation and the underlying evolution of galaxies through cosmic time. © 2025, CC BY.

关键词： Cosmic rays

Empowering Optimal Control with Machine Learning: A Perspective from Model Predictive Control

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IFAC-PapersOnLine 2022年第30期55卷 121-126页

作者： E Weinan Jiequn Han Jihao Long AI for Science Institute Beijing Center for Machine Learning Research and School of Mathematical Sciences Peking University Beijing China Center for Computational Mathematics Flatiron Institute New York 10010 USA Program of Applied and Computational Mathematics Princeton University Princeton 08544 USA

Solving complex optimal control problems have confronted computational challenges for a long time. Recent advances in machine learning have provided us with new opportunities to address these challenges. This paper takes model predictive control, a popular optimal control method, as the primary example to survey recent progress that leverages machine learning techniques to empower optimal control solvers. We also discuss some of the main challenges encountered when applying machine learning to develop more robust optimal control algorithms.

关键词： Machine Learning Optimal Control Model Predictive Control

Exploring the predictability of range-based volatility estimators using RNNs

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arXiv 2018年

作者： Petneházi, Gábor Gáll, József Doctoral School of Mathematical and Computational Sciences University of Debrecen Hungary Department of Applied Mathematics and Probability Theory University of Debrecen Hungary

We investigate the predictability of several range-based stock volatility estimators, and compare them to the standard close-to-close estimator which is most commonly acknowledged as the volatility. The patterns of volatility changes are analyzed using LSTM recurrent neural networks, which are a state of the art method of sequence learning. We implement the analysis on all current constituents of the Dow Jones Industrial Average index, and report averaged evaluation results. We find that changes in the values of range-based estimators are more predictable than that of the estimator using daily closing values only. Copyright © 2018, The Authors. All rights reserved.

关键词： Long short-term memory

Volatility forecasting with 1-dimensional CNNS via transfer learning

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arXiv 2020年

作者： Aradi, Bernadett Petneházi, Gábor Gáll, József Department of Applied Mathematics and Probability Theory University of Debrecen Hungary Doctoral School of Mathematical and Computational Sciences University of Debrecen Hungary

Volatility is a natural risk measure in finance as it quantifies the variation of stock prices. A frequently considered problem in mathematical finance is to forecast different estimates of volatility. What makes it promising to use deep learning methods for the prediction of volatility is the fact, that stock price returns satisfy some common properties, referred to as ‘stylized facts’. Also, the amount of data used can be high, favoring the application of neural networks. We used 10 years of daily prices for hundreds of frequently traded stocks, and compared different CNN architectures: some networks use only the considered stock, but we tried out a construction which, for training, uses much more series, but not the considered stocks. Essentially, this is an application of transfer learning, and its performance turns out to be much better in terms of prediction error. We also compare our dilated causal CNNs to the classical ARIMA method using an automatic model selection procedure. Copyright © 2020, The Authors. All rights reserved.

关键词： Financial markets

Path properties of atmospheric transitions: illustration with a low-order sudden stratospheric warming model

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arXiv 2020年

作者： Finkel, Justin Abbot, Dorian S. Weare, Jonathan Committee on Computational and Applied Mathematics University of Chicago Department of the Geophysical Sciences University of Chicago Courant Institute of Mathematical Sciences New York University

Many rare weather events, including hurricanes, droughts, and floods, dramatically impact human life. To accurately forecast these events and characterize their climatology requires specialized mathematical techniques to fully leverage the limited data that are available. Here we describe transition path theory (TPT), a framework originally developed for molecular simulation, and argue that it is a useful paradigm for developing mechanistic understanding of rare climate events. TPT provides a method to calculate statistical properties of the paths into the event. As an initial demonstration of the utility of TPT, we analyze a low-order model of sudden stratospheric warming (SSW), a dramatic disturbance to the polar vortex which can induce extreme cold spells at the surface in the midlatitudes. SSW events pose a major challenge for seasonal weather prediction because of their rapid, complex onset and development. Climate models struggle to capture the long-term statistics of SSW, owing to their diversity and intermittent nature. We use a stochastically forced Holton-Mass-type model with two stable states, corresponding to radiative equilibrium and a vacillating SSW-like regime. In this stochastic bistable setting, from certain probabilistic forecasts TPT facilitates estimation of dominant transition pathways and return times of transitions. These "dynamical statistics" are obtained by solving partial differential equations in the model’s phase space. With future application to more complex models, TPT and its constituent quantities promise to improve the predictability of extreme weather events, through both generation and principled evaluation of forecasts. Copyright © 2020, The Authors. All rights reserved.

关键词： Phase space methods

Time-frequency analysis and determinantal point processes 13

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Time-frequency analysis and determinantal point processes

13th International Conference on Theoretical and computational Acoustics, ICTCA 2017

作者： Abreu, Luís Daniel Gröchenig, Karlheinz Pereira, João M. Romero, José L. Torquato, Salvatore Acoustics Research Institute Austrian Academy of Sciences Vienna Austria NuHAG Faculty of Mathematics University of Vienna Vienna Austria Program in Applied and Computational Mathematics Princeton University New Jersey United States Princeton Materials Institute Department of Chemistry Princeton University New Jersey United States

Learning forecasts of rare stratospheric transitions from short simulations

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arXiv 2021年

作者： Finkel, Justin Webber, Robert J. Gerber, Edwin P. Abbot, Dorian S. Weare, Jonathan Committee on Computational and Applied Mathematics University of Chicago Department of Computing and Mathematical Sciences California Institute of Technology Courant Institute of Mathematical Sciences New York University Department of the Geophysical Sciences University of Chicago

Rare events arising in nonlinear atmospheric dynamics remain hard to predict and attribute. We address the problem of forecasting rare events in a prototypical example, Sudden Stratospheric Warmings (SSWs). Approximately once every other winter, the boreal stratospheric polar vortex rapidly breaks down, shifting midlatitude surface weather patterns for months. We focus on two key quantities of interest: the probability of an SSW occurring, and the expected lead time if it does occur, as functions of initial condition. These optimal forecasts concretely measure the event's progress. Direct numerical simulation can estimate them in principle, but is prohibitively expensive in practice: each rare event requires a long integration to observe, and the cost of each integration grows with model complexity. We describe an alternative approach using integrations that are short compared to the timescale of the warming event. We compute the probability and lead time efficiently by solving equations involving the transition operator, which encodes all information about the dynamics. We relate these optimal forecasts to a small number of interpretable physical variables, suggesting optimal measurements for forecasting. We illustrate the methodology on a prototype SSW model developed by Holton and Mass (1976) and modified by stochastic forcing. While highly idealized, this model captures the essential nonlinear dynamics of SSWs and exhibits the key forecasting challenge: the dramatic separation in timescales between a single event and the return time between successive events. Our methodology is designed to fully exploit high-dimensional data from models and observations, and has the potential to identify detailed predictors of many complex rare events in meteorology. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

Thermodynamic Limit of a Nonequilibrium Steady State: Maxwell-Type Construction for a Bistable Biochemical System

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Physical Review Letters 2009年第14期103卷 148103-148103页

作者： Hao Ge Hong Qian School of Mathematical Sciences and Centre for Computational Systems Biology Fudan University Shanghai 200433 People’s Republic of China Department of Applied Mathematics University of Washington Seattle Washington 98195 USA

We show that the thermodynamic limit of a bistable phosphorylation-dephosphorylation cycle has a selection rule for the “more stable” macroscopic steady state. The analysis is akin to the Maxwell construction. Based on the chemical master equation approach, it is shown that, except at a critical point, bistability disappears in the stochastic model when fluctuation is sufficiently low but unneglectable. Onsager’s Gaussian fluctuation theory applies to the unique macroscopic steady state. With an initial state in the basin of attraction of the “less stable” steady state, the deterministic dynamics obtained by the law of mass action is a metastable phenomenon. Stability and robustness in cell biology are emergent stochastic concepts.

关键词： Stochastic models

An approximate series solution of the semiclassical Wigner equation

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arXiv 2017年

作者： Giannopoulou, K.S. Makrakis, G.N. Department of Mathematical Sciences Norwegian University of Science and Technology Trondheim Norway Department of Mathematics & Applied Mathematics University of Crete Heraklion Crete Greece Institute of Applied and Computational Mathematics FORTH Heraklion Crete Greece

We propose a new approximate series solution of the semiclassical Wigner equation by uniformization of WKB approximations of the Schrödinger eigenfunctions. Copyright © 2017, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions