A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Optimizes hydrokinetic turbine design using morphing and Bayesian optimization.
Study assesses risk of upward lightning at wind turbines using direct measurements and machine learning.
Paper uses neural networks to predict NOx emissions from gas turbines.
We prove a modified version of Turbiner's conjecture in three dimensions and we give a counter-example to the original conjecture. The Lie algebraic Schrödinger operators corresponding to flat metrics of a certain restricted type are shown to separate partially in either Cartesian, cylindrical or spherical coordinates.
A detailed understanding of wind turbine performance status classification can improve operations and maintenance in the wind energy industry. Due to different engineering properties of wind turbines, the standard supervised learning models used for classification do not generalize across data sets obtained from differ…
We introduce a measure for estimating the best risk-return relation of power production in wind farms within a given time-lag, conditioned to the velocity field. The velocity field is represented by a scalar that weighs the influence of the velocity at each wind turbine at present and previous time-steps for the presen…
The installation process of offshore wind turbines requires the use of expensive jack-up vessels. These vessels regularly report their position via the Automatic Identification System (AIS). This paper introduces a novel approach of applying machine learning to AIS data from jack-up vessels. We apply the new method to …
Deep learning has been used in many areas, such as feature detections in images and the game of go. This paper presents a study that attempts to use the deep learning method to predict turbomachinery performance. Three different deep neural networks are built and trained to predict the pressure distributions of turbine…
Monitoring gas turbine combustors health, in particular, early detecting abnormal behaviors and incipient faults, is critical in ensuring gas turbines operating efficiently and in preventing costly unplanned maintenance. One popular means of detecting combustor abnormalities is through continuously monitoring exhaust g…
Wind power, as an alternative to burning fossil fuels, is abundant and inexhaustible. To fully utilize wind power, wind farms are usually located in areas of high altitude and facing serious ice conditions, which can lead to serious consequences. Quick detection of blade ice accretion is crucial for the maintenance of …
The cost of wind energy can be reduced by using SCADA data to detect faults in wind turbine components. Normal behavior models are one of the main fault detection approaches, but there is a lack of consensus in how different input features affect the results. In this work, a new taxonomy based on the causal relations b…
LOL-GP model improves surrogate modeling of expensive simulators.
We propose a novel Bayesian approach to modelling nonlinear alignments of time series based on latent shared information. We apply the method to the real-world problem of finding common structure in the sensor data of wind turbines introduced by the underlying latent and turbulent wind field. The proposed model allows …
Bayesian method models multivalued power data from wind farms.
Condition monitoring is central to the efficient operation of wind farms due to the challenging operating conditions, rapid technology development and large number of aging wind turbines. In particular, predictive maintenance planning requires the early detection of faults with few false positives. Achieving this type …
Bayesian optimization identifies optimal alloy formulations.
New algorithm learns coordinated decisions in loosely-coupled multi-agent systems.
New scalarizing functions improve multi-objective Bayesian optimisation.
Study predicts wind energy potential in Gulf of Oman using climate models.
Wind power prediction is of vital importance in wind power utilization. There have been a lot of researches based on the time series of the wind power or speed, but In fact, these time series cannot express the temporal and spatial changes of wind, which fundamentally hinders the advance of wind power prediction. In th…
Turbiner's conjecture posits that a Lie-algebraic Hamiltonian operator whose domain is a subset of the Euclidean plane admits a separation of variables. A proof of this conjecture is given in those cases where the generating Lie-algebra acts imprimitively. The general form of the conjecture is false. A counter-example …
Wind farm layout optimisation tackles space constraints with Bayesian multi-objective approach.
This paper improves surrogate modeling for noisy data.
REMAL: Residual Equilibrium Manifold Active Learning for Surrogate-Based Multidisciplinary Design Analysis
MLM models match or exceed RN in generating wind power time series without location info.
Computer model calibration typically operates by choosing parameter values in a computer model so that the model output faithfully predicts reality. By using performance targets in place of observed data, we show that calibration techniques can be repurposed to wed engineering and material design, two processes that ar…
We present an algorithm for model-based reinforcement learning that combines Bayesian neural networks (BNNs) with random roll-outs and stochastic optimization for policy learning. The BNNs are trained by minimizing -divergences, allowing us to capture complicated statistical patterns in the transition dynamics, e.g.…
Study uses ANFIS to assess wind power under climate change.
This paper optimizes a power-to-heat system using reinforcement learning for cost minimization under uncertain conditions.
Compact models for methane/air combustion reduce complexity without sacrificing accuracy.
This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamic filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependen…
In this article we present an approach that enables joint wind speed and wind power forecasts for a wind park. We combine a multivariate seasonal time varying threshold autoregressive moving average (TVARMA) model with a power threshold generalized autoregressive conditional heteroscedastic (power-TGARCH) model. The mo…
The study optimizes wind farm yaw control using Gaussian process regression and high-fidelity simulations.
Incorporating computational fluid dynamics in the design process of jets, spacecraft, or gas turbine engines is often challenged by the required computational resources and simulation time, which depend on the chosen physics-based computational models and grid resolutions. An ongoing problem in the field is how to simu…
Method reconstructs missing wind farm data using graph theory and nearest neighbors.
New algorithm reduces costs in wind energy systems by minimizing decision changes.
Paper introduces a new model to handle multi-task learning across different input domains.
DeepMIDE forecasts wind speeds across space, time, and height for offshore wind energy.
Superintegrable systems are classical and quantum Hamiltonian systems which enjoy much symmetry and structure that permit their solubility via analytic and even, algebraic means. They include such well-known and important models as the Kepler potential, Calogero-Moser model, and harmonic oscillator, as well as its inte…
mNARX+ creates accurate surrogate models for complex systems without requiring domain expertise.
A method to improve surrogate model accuracy using multiple fidelity models.
ROM-net framework applies to industrial design uncertainty quantification.
Paper tackles temporal overfitting in wind power curve modeling.
The paper compares DL models to WP curve modeling for forecasting with irregular shutdowns.
Bayesian method maps high-dimensional inputs to lower dimensions for efficient multi-fidelity Gaussian Process modeling.
A novel ensemble classifier improves vibration-based quality monitoring accuracy.
This paper tackles reliability analysis for stochastic systems using surrogate models.
Proposes ICE-based metric for better understanding interactions in black-box models.