Siste publikasjoner - Beregningsorientert matematikk

• Di Nunno, Giulia & Gianin, Emanuela Rosazza (2024). Fully Dynamic Risk Measures: Horizon Risk, Time-Consistency, and Relations with BSDEs and BSVIEs. SIAM Journal on Financial Mathematics. ISSN 1945-497X. 15(2), s. 399–435. doi: 10.1137/23M1546804.
• Savku, Emel (2024). Deep-Control of Memory via Stochastic Optimal Control and Deep Learning. I Martinez, Viktor Gayoso; Queiruga-Dios, Araceli; Martin-Vaquero, Jesus; Yilmaz, Fatih; Rasteiro, Deolinda & Mierlus-Mazilu, Ion (Red.), Mathematical Methods for Engineering Applications ICMASE 2023, Madrid, Spain, July 12–14. Springer Nature. ISSN 978-3-031-49217-4. s. 219–240. doi: 10.1007/978-3-031-49218-1_16.
• Tymoshenko, Olena; Proske, Frank Norbert & Orlovskyi, Igor (2024). Long-time behaviors of some stochastic differential equations driven by Lévy noise. arXiv.org. ISSN 2331-8422. doi: 10.48550/arXiv.2402.05594.
• Vorobeva, Ekaterina; Eggen, Mari Dahl; Midtfjord, Alise Danielle; Benth, Fred Espen; Hupe, Patrick & Brissaud, Quentin [Vis alle 8 forfattere av denne artikkelen] (2024). Estimating stratospheric polar vortex strength using ambient ocean-generated infrasound and stochastics-based machine learning. Quarterly Journal of the Royal Meteorological Society. ISSN 0035-9009. doi: 10.1002/qj.4731. Fulltekst i vitenarkiv Vis sammendrag

  There are sparse opportunities for direct measurement of upper stratospheric winds, yet improving their representation in subseasonal-to-seasonal prediction models can have significant benefits. There is solid evidence from previous research that global atmospheric infrasound waves are sensitive to stratospheric dynamics. However, there is a lack of results providing a direct mapping between infrasound recordings and polar-cap upper stratospheric winds. The global International Monitoring System (IMS), which monitors compliance with the Comprehensive Nuclear-Test-Ban Treaty, includes ground-based stations that can be used to characterize the infrasound soundscape continuously. In this study, multi-station IMS infrasound data were utilized along with a machine-learning supported stochastic model, Delay-SDE-net, to demonstrate how a near-real-time estimate of the polar-cap averaged zonal wind at 1-hPa pressure level can be found from infrasound data. The infrasound was filtered to a temporal low-frequency regime dominated by microbaroms, which are ambient-noise infrasonic waves continuously radiated into the atmosphere from nonlinear interaction between counter-propagating ocean surface waves. Delay-SDE-net was trained on 5 years (2014–2018) of infrasound data from three stations and the ERA5 reanalysis 1-hPa polar-cap averaged zonal wind. Using infrasound in 2019–2020 for validation, we demonstrate a prediction of the polar-cap averaged zonal wind, with an error standard deviation of around 12 m·s compared with ERA5. These findings highlight the potential of using infrasound data for near-real-time measurements of upper stratospheric dynamics. A long-term goal is to improve high-top atmospheric model accuracy, which can have significant implications for weather and climate prediction.

• Benth, Fred Espen; Schroers, Dennis & Veraart, Almut E.D. (2024). A feasible central limit theorem for realised covariation of SPDEs in the context of functional data. The Annals of Applied Probability. ISSN 1050-5164. 34(2), s. 2208–2242. doi: 10.1214/23-AAP2019.
• Amine, Oussama; Baños, David & Proske, Frank Norbert (2024). C∞-Regularization by Noise of Singular ODE’s. Journal of Dynamics and Differential Equations. ISSN 1040-7294. doi: 10.1007/s10884-024-10355-w.
• Grochowicz, Aleksander; van Greevenbroek, Koen & Bloomfield, Hannah C. (2024). Using power system modelling outputs to identify weather-induced extreme events in highly renewable systems. Environmental Research Letters. ISSN 1748-9326. 19(5), s. 1–15. doi: 10.1088/1748-9326/ad374a. Fulltekst i vitenarkiv
• Ren, Panpan; Tang, Hao & Wang, Feng-Yu (2024). Distribution-Path Dependent Nonlinear SPDEs with Application to Stochastic Transport Type Equations. Potential Analysis. ISSN 0926-2601. doi: 10.1007/s11118-023-10113-5.
• Pilipenko, Andrey; Mishura, Yuliya & Yurchenko-Tytarenko, Anton (2024). Low-dimensional Cox-Ingersoll-Ross process. Stochastics: An International Journal of Probability and Stochastic Processes. ISSN 1744-2508. doi: 10.1080/17442508.2023.2300291. Fulltekst i vitenarkiv
• Yurchenko-Tytarenko, Anton & Di Nunno, Giulia (2024). Power law in Sandwiched Volterra Volatility model. Modern Stochastics: Theory and Applications (MSTA). ISSN 2351-6046. 11(2), s. 169–194. doi: 10.15559/24-VMSTA246. Fulltekst i vitenarkiv
• Compagnoni, Enea; Orvieto, Antonio; Kersting, Hans; Proske, Frank Norbert & Lucchi, Aurelien (2024). SDEs for Minimax Optimization. Proceedings of Machine Learning Research (PMLR). ISSN 2640-3498. 206.
• Grochowicz, Aleksander; Benth, Fred Espen & Zeyringer, Marianne (2024). Spatio-temporal smoothing and dynamics of different electricity flexibility options for highly renewable energy systems—Case study for Norway. Applied Energy. ISSN 0306-2619. 356. doi: 10.1016/j.apenergy.2023.122338. Vis sammendrag

  In this article, we investigate the mismatch of renewable electricity production to demand and how flexibility options enabling spatial and temporal smoothing can reduce risks of variability. As a case study we pick a simplified (partial) 2-region representation of the Norwegian electricity system and focus on wind power. We represent regional electricity production and demand through two stochastic processes: the wind capacity factors are modelled as a two-dimensional Ornstein–Uhlenbeck process and electricity demand consists of realistic base load and temperature-induced load coming from a deseasonalised autoregressive process. We validate these processes, that we have trained on historical data, through Monte Carlo simulations allowing us to generate many statistically representative weather years. For the investigated realisations (weather years) we study deviations of production from demand under different wind capacities, and introduce different scenarios where flexibility options like storage and transmission are available. Our analysis shows that simulated loss values are reduced significantly by cooperation between regions and either mode of flexibility. Combining storage and transmission leads to even more synergies and helps to stabilise production levels and thus reduces likelihoods of inadequacy of renewable power systems.

• Amine, Oussama; Baños, David Ruiz & Proske, Frank Norbert (2024). C-infinity-regularization by Noise of Singular ODE's. Journal of Dynamics and Differential Equations. ISSN 1040-7294. Fulltekst i vitenarkiv
• Øksendal, Bernt Karsten (2023). Space-time stochastic calculus and white noise. I Morel, Jean-Michel & Teissier, Bernard (Red.), Mathematics Going Forward - Collected Mathematical Brushstrokes. Springer. ISSN 978-3-031-12243-9. s. 629–649. Fulltekst i vitenarkiv
• Sande, Åsmund Hausken (2023). Convex environmental contours for non-stationary processes. Ocean Engineering. ISSN 0029-8018. 292. doi: 10.1016/j.oceaneng.2023.116615. Fulltekst i vitenarkiv
• Banos, David Ruiz; Sande, Åsmund Hausken & Sgarra, Carlo (2023). Guaranteed Minimum Maturity Benefits in a Self-Exciting Stochastic Mortality Model: Pricing, Estimation and Calibration. North American Actuarial Journal (NAAJ). ISSN 1092-0277. doi: 10.1080/10920277.2023.2254836. Fulltekst i vitenarkiv
• Bogso, Antoine-Marie; Menoukeu-Pamen, Olivier & Proske, Frank Norbert (2023). EXISTENCE OF STRONG SOLUTIONS OF FRACTIONAL BROWNIAN SHEET DRIVEN SDES WITH INTEGRABLE DRIFT. . arXiv.org. ISSN 2331-8422.
• Agram, Nacira; Øksendal, Bernt Karsten; Proske, Frank Norbert & Tymoshenko, Olena (2023). Optimal control of SPDEs driven by time-space Brownian motion . arXiv.org. ISSN 2331-8422.
• Compagnoni, Enea; Biggio, Luca; Orvieto, Antonio; Proske, Frank Norbert; Kersting, Hans & Lucchi, Aurelien (2023). An SDE for Modeling SAM: Theory and Insights. Proceedings of Machine Learning Research (PMLR). ISSN 2640-3498. 202, s. 25209–25253. Fulltekst i vitenarkiv
• Huseby, Arne Bang (2023). Environmental contours and time dependence. I Brito, Mario P.; Aven, Terje; Baraldi, Piero; Cepin, Marko & Zio, Enrico (Red.), ESREL 2023 - Proceedings of the 33rd European Safety and Reliability Conference : The Future of Safety in the Reconnected World, 3 – 7 September 2023, University of Southampton, United Kingdom. Research Publishing Services. ISSN 978-981-18-8071-1. s. 1295–1302. doi: 10.3850/978-981-18-8071-1_P048-cd. Vis sammendrag

  Environmental contours are widely used as a basis for e.g., ship design. Such contours are typically used in early design when the strength and failure properties of the object under consideration are not known. An environmental contour describes the tail properties of some relevant environmental variables, and is used as input to the design process. A methodology for constructing environmental contours based on the Rosenblatt transformation has beed used extensively. More recently alternative approach where environmental contours are constructed using Monte Carlo simulation have been developed. Typically, the strength of a structural design is chosen so that the expected return period of a failure event exceeds the desired lifetime of the structure. If time dependence in the environmental variables is neglected, the expected return period is simply the inverse of the failure probability. In a more realistic model, however, such dependence should be included. In this paper we describe a method for constructing an environmental contour where time dependence is taken into account. The method is illustrated with a simple numerical example.

• Agram, Nacira & Øksendal, Bernt Karsten (2023). The Donsker delta function and local time for McKean–Vlasov processes and applications. Stochastics: An International Journal of Probability and Stochastic Processes. ISSN 1744-2508. doi: 10.1080/17442508.2023.2286252. Fulltekst i vitenarkiv
• Benth, Fred Espen; Detering, Nils & Galimberti, Luca (2023). Pricing options on flow forwards by neural networks in a Hilbert space. Finance and Stochastics. ISSN 0949-2984. 28, s. 81–121. doi: 10.1007/s00780-023-00520-2.
• Giordano, Michele & Yurchenko-Tytarenko, Anton (2023). Optimal control in linear-quadratic stochastic advertising models with memory. Decisions in Economics and Finance (DAF). ISSN 1593-8883. doi: 10.1007/s10203-023-00409-x. Fulltekst i vitenarkiv
• Dordevic, Jasmina (2023). Backward Doubly Stochastic Integral Equations of the Volterra Type and Some Related Problems. Communications in Mathematics and Statistics. ISSN 2194-6701. doi: 10.1007/s40304-023-00349-3. Fulltekst i vitenarkiv
• Catellier, Rémi & Harang, Fabian Andsem (2023). Pathwise regularization of the stochastic heat equation with multiplicative noise through irregular perturbation. Annales de l'I.H.P. Probabilites et statistiques. ISSN 0246-0203. 59(3), s. 1572–1609. doi: 10.1214/22-AIHP1302. Fulltekst i vitenarkiv Vis sammendrag

  Existence and uniqueness of solutions to the stochastic heat equation with multiplicative spatial noise is studied. In the spirit of pathwise regularization by noise, we show that a perturbation by a sufficiently irregular continuous path establish wellposedness of such equations, even when the drift and diffusion coefficients are given as generalized functions or distributions. In addition we prove regularity of the averaged field associated to a Lévy fractional stable motion, and use this as an example of a perturbation regularizing the multiplicative stochastic heat equation.

• Di Nunno, Giulia; Kubilius, Kestutis; Mishura, Yuliya & Yurchenko-Tytarenko, Anton (2023). From Constant to Rough: A Survey of Continuous Volatility Modeling. Mathematics. ISSN 2227-7390. 11(19). doi: 10.3390/math11194201. Fulltekst i vitenarkiv Vis sammendrag

  In this paper, we present a comprehensive survey of continuous stochastic volatility models, discussing their historical development and the key stylized facts that have driven the field. Special attention is dedicated to fractional and rough methods: without advocating for either roughness or long memory, we outline the motivation behind them and characterize some landmark models. In addition, we briefly touch on the problem of VIX modeling and recent advances in the SPX-VIX joint calibration puzzle.

• Amine, Oussama; Baghery, Karim; Pindado, Zaira & Ràfols, Carla (2023). Simulation extractable versions of Groth’s zk-SNARK revisited. International Journal of Information Security. ISSN 1615-5262. 23, s. 431–445. doi: 10.1007/s10207-023-00750-7. Fulltekst i vitenarkiv
• Mishura, Yuliya & Yurchenko-Tytarenko, Anton (2023). Parameter Estimation in Rough Bessel Model. Fractal and Fractional. 7(7). doi: 10.3390/fractalfract7070508. Fulltekst i vitenarkiv
• Tang, Hao (2023). On the stochastic Euler-Poincaré equations driven by pseudo-differential/multiplicative noise. Journal of Functional Analysis. ISSN 0022-1236. 285(9). doi: 10.1016/j.jfa.2023.110075. Fulltekst i vitenarkiv
• Benth, Fred Espen & Lempa, Jukka (2023). Hedging temperature risk with CDD and HDD temperature futures. Applied Stochastic Models in Business and Industry. ISSN 1524-1904. doi: 10.1002/asmb.2815. Fulltekst i vitenarkiv
• Bordin, Chiara; Mishra, Sambeet & Benth, Fred Espen (2023). Pedagogical Perspectives of Interdisciplinary Teaching and Research: An Energy System Modelling Outlook in Relation to Energy Informatics. Energies. ISSN 1996-1073. 16(15). doi: 10.3390/en16155757. Fulltekst i vitenarkiv
• Puica, Mihaela-Alexandra & Benth, Fred Espen (2023). A spatio-temporal model for predicting wind speeds in Southern California. Communications in statistics. Case studies, data analysis and applications.. ISSN 2373-7484. 9(3), s. 321–349. doi: 10.1080/23737484.2023.2217137.
• Coffie, Emmanuel; Mao, Xuerong & Proske, Frank Norbert (2023). On the Analysis of Ait-Sahalia-Type Model for Rough Volatility Modelling. Journal of theoretical probability. ISSN 0894-9840. doi: 10.1007/s10959-023-01269-2. Fulltekst i vitenarkiv
• Savku, Emel (2023). A Stochastic Control Approach for Constrained Stochastic Differential Games with Jumps and Regimes. Mathematics. ISSN 2227-7390. 11(14). doi: 10.3390/math11143043. Fulltekst i vitenarkiv
• Benth, Fred Espen; Deelstra, Griselda & Kozplnar, And Sinem (2023). Pricing energy quanto options in the framework of Markov-modulated additive processes. IMA Journal of Management Mathematics. ISSN 1471-678X. 34(1), s. 187–220. doi: 10.1093/imaman/dpab032.
• Makhlouf, K.; Agram, Nacira; Hilbert, A. & Øksendal, Bernt Karsten (2023). SPDEs with space interactions and application to population modelling. ESAIM: Control, Optimisation and Calculus of Variations (ESAIM: COCV). ISSN 1292-8119. 29. doi: 10.1051/cocv/2023010. Fulltekst i vitenarkiv
• Agram, Nacira & Øksendal, Bernt Karsten (2023). Stochastic Fokker–Planck Equations for Conditional McKean–Vlasov Jump Diffusions and Applications to Optimal Control. SIAM Journal of Control and Optimization. ISSN 0363-0129. 61(3), s. 1472–1493. doi: 10.1137/21M1461034. Fulltekst i vitenarkiv
• Benth, Fred Espen & Karbach, Sven (2023). Multivariate continuous-time autoregressive moving-average processes on cones. Stochastic Processes and their Applications. ISSN 0304-4149. 162, s. 299–337. doi: 10.1016/j.spa.2023.05.003.
• Moulay Hachemi, Rahma Yasmina & Øksendal, Bernt Karsten (2023). The fractional stochastic heat equation driven by time-space white noise. Fractional Calculus and Applied Analysis. ISSN 1311-0454. 26(2), s. 513–532. doi: 10.1007/s13540-023-00134-7. Fulltekst i vitenarkiv
• Puica, Mihaela-Alexandra; Dehant, V.; Folgueira, M.; Van Hoolst, Hoolst & Rekier, J. (2023). Analytical computation of total topographic torque at the core-mantle boundary and its impact on tidally driven length-of-day variations. Geophysical Journal International. ISSN 0956-540X. 234(1), s. 585–596. doi: 10.1093/gji/ggad077. Fulltekst i vitenarkiv
• Di Nunno, Giulia & Giordano, Michele (2023). Stochastic Volterra equations with time-changed Lévy noise and maximum principles. Annals of Operations Research. ISSN 0254-5330. doi: 10.1007/s10479-023-05303-8. Fulltekst i vitenarkiv Vis sammendrag

  Motivated by a problem of optimal harvesting of natural resources, we study a control problem for Volterra type dynamics driven by time-changed Lévy noises, which are in general not Markovian. To exploit the nature of the noise, we make use of different kind of information flows within a maximum principle approach. For this we work with backward stochastic differential equations (BSDE) with time-change and exploit the non-anticipating stochastic derivative introduced in Di Nunno and Eide (Stoch Anal Appl 28:54-85, 2009). We prove both a sufficient and necessary stochastic maximum principle.

• Agrell, Christian; Dahl, Kristina Rognlien & Hafver, Andreas (2023). Optimal sequential decision making with probabilistic digital twins. SN Applied Sciences. ISSN 2523-3963. 5(4). doi: 10.1007/s42452-023-05316-9. Fulltekst i vitenarkiv Vis sammendrag

  In this study, we present a formal defnition of the probabilistic digital twin (PDT). Digital twins are emerging in many industries, typically consisting of simulation models and data associated with a specifc physical system. In order to defne probabilistic digital twins, we discuss how epistemic uncertainty can be treated using measure theory, by modelling epistemic information via sigma-algebras. A gentle introduction to the necessary mathematical theory is provided throughout the paper, together with a number of examples to illustrate the core concepts. We then introduce the problem of optimal sequential decision making. That is, when the outcome of each decision may inform the next. We discuss how this problem may be solved theoretically, and the current limitations that prohibit most practical applications. As a numerically tractable alternative, we propose a generic approximate solution using deep reinforcement learning together with neural networks defined on sets. We illustrate the method on a practical problem, considering optimal information gathering for the estimation of a failure probability.

• Lindstrøm, Tom Louis (2023). The Allure of Infinitesimals: Sergio Albeverio and Nonstandard Analysis. I Hilbert, Astrid; Mastrogiacomo, Elisa; Mazzucchi, Sonia; Rüdiger, Barbara & Ugolini, Stefania (Red.), Quantum and Stochastic Mathematical Physics. Springer. ISSN 978-3-031-14031-0. s. 187–215. doi: 10.1007/978-3-031-14031-0_9. Fulltekst i vitenarkiv
• Amine, Oussama; Mansouri, Abdol-Reza & Proske, Frank Norbert (2023). Well-posedness of the deterministic transport equation with singular velocity field perturbed along fractional Brownian paths. Journal of Differential Equations. ISSN 0022-0396. 362, s. 106–172. doi: 10.1016/j.jde.2023.02.059. Fulltekst i vitenarkiv
• Miao, Yingting; Rohde, Christian & Tang, Hao (2023). Well-posedness for a stochastic Camassa–Holm type equation with higher order nonlinearities. Stochastics and Partial Differential Equations: Analysis and Computations. ISSN 2194-0401. doi: 10.1007/s40072-023-00291-z. Fulltekst i vitenarkiv
• Di Nunno, Giulia; Ortiz-Latorre, Salvador & Petersson, Andreas Erik (2023). SPDE bridges with observation noise and their spatial approximation. Stochastic Processes and their Applications. ISSN 0304-4149. 158, s. 170–207. doi: 10.1016/j.spa.2023.01.007. Fulltekst i vitenarkiv

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• Benth, Fred Espen & Krühner, Paul (2023). Stochastic Models for Prices Dynamics in Energy and Commodity Markets An Infinite-Dimensional Perspective. Springer. ISBN 978-3-031-40366-8. 250 s.

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• Viole, Isabelle; van Greevenbroek, Koen & Cheng, Claudia Siew Wan (2024). Can Norway save the European Union's hydrogen ambition for 2030?
• Lindstrøm, Tom Louis (2024). Historien om å løse ligninger.
• Yurchenko-Tytarenko, Anton (2024). Quadratic hedging in SVV-model.
• Zamora Font, Oriol; Baños, David & Ortiz-Latorre, Salvador (2023). Heston-Hawkes stochastic volatility model: Change of measure and Thiele's PIDE.
• Ortiz-Latorre, Salvador (2023). Strong solutions for singular SDEs driven by fractional Brownian motion.
• Huseby, Arne Bang (2023). Domination and multistate systems. Vis sammendrag

  Domination theory has been studied extensively in the context of binary monotone systems, where the structure function is a sum of products of the component states, and with coefficients given by the signed domination function. Using e.g., matroid theory, many useful properties of the signed domination function can be derived. In this paper we show how some of these results can be extended to multistate systems. The signed domination function is an invariant determined by the poset generated by the minimal path vectors. Two systems with isomorphic posets also have the same signed domination function. We derive an explicit formula for the domination using Möbius functions. Furthermore, we obtain a recursive formula for the signed domination function, referred to as the signed domination theorem. Both these results are generalizations of the corresponding results for binary monotone systems.

• Grochowicz, Aleksander (2023). Spatio-temporal smoothing and dynamics of different electricity flexibility options.
• Grochowicz, Aleksander (2023). Identifying weather stress events from power system optimisation outputs.
• Grochowicz, Aleksander; Greevenbroek, Koen van & Bloomfield, Hannah C. (2023). Identifying weather stress events from power system optimisation outputs.
• Petersson, Andreas Erik (2023). Noise Simulation in Finite Element Methods for SPDE Approximation.
• Petersson, Andreas Erik (2023). Piecewise linear interpolation of noise in finite element approximations of parabolic SPDEs.
• Petersson, Andreas Erik (2023). SPDE bridges with observation noise and their spatial approximation.
• Petersson, Andreas Erik (2023). The HEat modulated Infinite DImensional Heston (HEIDIH) model and its numerical approximation.
• Savku, Emel (2023). A Nonzero-Sum Regime-Switching Stochastic Differential Game Application with Constraints.
• Di Nunno, Giulia (2023). On stochastic control for time changed Lévy dynamics.
• Di Nunno, Giulia (2023). Horizon risk and fully dynamic risk measures.
• Di Nunno, Giulia (2023). Pricing in sandwiched Volterra volatility models.
• Di Nunno, Giulia (2023). Dynamic risk assessment, horizon risk, and interest rate uncertainty.
• Di Nunno, Giulia (2023). Sandwiched Volterra volatility models and hedging.
• Di Nunno, Giulia (2023). Sandwiched Volterra volatility models and hedging.
• Di Nunno, Giulia (2023). Sandwiched Volterra volatility models and hedging.
• Di Nunno, Giulia (2023). Optimal control for Volterra type dynamics.
• Di Nunno, Giulia (2023). Sandwiched Volterra volatility models and option pricing.
• Di Nunno, Giulia (2023). Lifting of Volterra processes: Optimal control in UMD Banach spaces .
• Di Nunno, Giulia (2023). Research on Risk Measures.
• Di Nunno, Giulia (2023). Horizon risk and dynamic risk assessment.
• Di Nunno, Giulia (2023). Horizon risk and fully dynamic risk measures.
• Ortiz-Latorre, Salvador (2023). Time discretizations for the nonlinear filtering problem.
• Ortiz-Latorre, Salvador (2023). Robust discretizations for the solution of the non-linear filtering problem.
• Ortiz-Latorre, Salvador (2023). SPDE bridges with observation noise and their spatial approximation.
• Ortiz-Latorre, Salvador (2023). Robust approximations for the solution of the non-linear filtering problem.
• Ortiz-Latorre, Salvador (2023). Strong solutions for singular SDEs driven by fractional Brownian motion.
• Yurchenko-Tytarenko, Anton (2023). Volterra sandwiched volatility model: Markovian approximation and hedging.
• Yurchenko-Tytarenko, Anton (2023). CIR process and Skorokhod problems]{Cox-Ingersoll-Ross process and Skorokhod problems.
• Yurchenko-Tytarenko, Anton (2023). Volterra sandwiched volatility model: Markovian approximation and hedging.
• Yurchenko-Tytarenko, Anton (2023). Volterra sandwiched volatility model: Markovian approximation and hedging.
• Yurchenko-Tytarenko, Anton (2023). Hedging in Volterra volatility models.
• Yurchenko-Tytarenko, Anton (2023). Parameter estimation in rough Bessel model.
• Yurchenko-Tytarenko, Anton (2023). Power law in SVV model.
• Diaz Lozano, Pere (2023). Neural SDEs for Conditional Time Series Generation using the Signature Wasserstein-1 metric.
• Grochowicz, Aleksander (2023). The role of historical weather data and how that can be used for a more resilient energy transition.
• Grochowicz, Aleksander; van Greevenbroek, Koen; Benth, Fred Espen & Zeyringer, Marianne (2023). Intersecting Near-Optimal Spaces for Policy Information.
• van Greevenbroek, Koen; Grochowicz, Aleksander & Bloomfield, Hannah C. (2023). Identifying Weather Stress Events from Power System Optimisation Outputs.
• Zamora Font, Oriol; Baños, David & Ortiz-Latorre, Salvador (2023). Heston-Hawkes Stochastic Volatility Model: Change of Measure and Forward Variance.
• Zamora Font, Oriol; Baños, David & Ortiz-Latorre, Salvador (2023). Heston-Hawkes stochastic volatility model: change of measure and forward variance.
• Greevenbroek, Koen van; Grochowicz, Aleksander; Zeyringer, Marianne; Noll, Josef & Vågerö, Oskar (2023). Vi treng uavhengig forsking, ikkje drøymetenking om kjernekraft. Aftenposten (morgenutg. : trykt utg.). ISSN 0804-3116.
• Grochowicz, Aleksander & Zeyringer, Marianne (2023). Managing the variability in time and space of renewable power generation.
• Grochowicz, Aleksander & Zeyringer, Marianne (2023). Managing the variability in time and geography of renewable power generation.
• Eggen, Mari Dahl; Midtfjord, Alise Danielle; Vorobeva, Ekaterina; Benth, Fred Espen; Hupe, Patrick & Brissaud, Quentin [Vis alle 10 forfattere av denne artikkelen] (2023). Using a machine learning and stochastics-founded model to provide near real-time stratospheric polar vortex diagnostics based on high-latitude infrasound data. Vis sammendrag

  Acoustic waves below the frequency limit of human hearing - infrasound - can travel for thousands of kilometres in the atmosphere. The global propagation signature of infrasound is highly sensitive to the wind structure of the stratosphere. This work exploits processed continuous data from three high-latitude infrasound stations to characterize an aspect of the stratospheric polar vortex. Concretely, a mapping is developed which takes the infrasound data from these three stations as input and outputs an estimate of the polar cap zonal mean wind averaged over 60-90 degrees in latitude at the 1 hPa pressure level. This stratospheric diagnostic information is relevant to, for example, sudden stratospheric warming assessment and sub-seasonal prediction. The considered acoustic data is within a low-frequency regime globally dominated by so-called microbarom infrasound, which is continuously radiated into the atmosphere due to nonlinear interaction between counter-propagating ocean surface waves. We trained a stochastics-based machine learning model (delay-SDE-net) to map between a time series of five years (2014-2018) of processed infrasound data and the ERA5 (reanalysis-based) daily average polar cap wind at 1 hPa for the same period. The ERA5 data was hence treated as ground-truth. In the prediction, the delay-SDE-net utilizes time-lagged inputs and their dependencies, as well as the day of the year to account for seasonal differences. In the validation phase, the input was the 2019 and 2020 infrasound time series, and the model inference results in an estimate of the daily average polar cap wind time-series. This result was then compared to the ERA5 representation of the stratospheric diagnostic time-series for the same period. The applied machine learning model is based on stochastics and allows for an interpretable approach to estimate the aleatoric and epistemic prediction uncertainties. It is found that the mapping, which is only informed of the trained model, the day of year, and the infrasound data from three stations, generates a 1 hPa polar cap average wind estimate with a prediction error standard deviation of around 10 m/s compared to ERA5. Focus should be put on the winter months because this is when the coupling between the stratosphere and the troposphere can mostly influence the surface conditions and provide additional prediction skill, in particular during strong and weak stratospheric polar vortex regimes. The infrasound data is available in real-time, and we discuss how the developed approach can be extended to provide near real-time stratospheric polar vortex diagnostics.

• Savku, Emel (2023). A Stochastic Maximum Principle Approach for a Nash Equilibrium of a Nonzero-Sum Game.
• Savku, Emel (2023). Stochastic Maximum Principle For A Constraint Nonzero-Sum Game Application:Bancassurance.
• van Greevenbroek, Koen; Zeyringer, Marianne; Noll, Josef; Grochowicz, Aleksander & Vågerö, Oskar (2023). Kjernekraft er ikkje naudsynt for Noreg. Det vil heller ikkje lønna seg. Aftenposten (morgenutg. : trykt utg.). ISSN 0804-3116.
• Grochowicz, Aleksander (2024). Resilience and weather uncertainty in energy system models. Universitetet i Oslo. ISSN 1501-7710. Fulltekst i vitenarkiv

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