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.
Paper quantifies uncertainties in EIS spectra of SOFCs, proposing VB method for online monitoring.
Bayesian modeling predicts hydroxide ion conductivity in polymer membranes.
AI speeds up hydrogen fuel cell stack development time.
This paper describes a pattern recognition approach aiming to estimate fuel cell duration time from electrochemical impedance spectroscopy measurements. It consists in first extracting features from both real and imaginary parts of the impedance spectrum. A parametric model is considered in the case of the real part, w…
Deep learning detects corrosion in nuclear fuel canisters.
Method detects effects of synthesis parameters on plutonium oxide microstructure.
We investigate Lie algebras endowed with a complex symplectic structure and develop a method, called \emph{complex symplectic oxidation}, to construct certain complex symplectic Lie algebras of dimension from those of dimension . We specialize this construction to the nilpotent case and apply complex symplec…
A soap film is actually a thin solid fluid bounded by two surfaces of opposite orientation. It is natural to model the film using one polyhedron for each side. Two problems are to get the polyhedra for both sides to be in the same place without canceling each other out and to model triple junctions without introducing …
Optimal scheduling of hydrogen production in dynamic pricing power market can maximize the profit of hydrogen producer; however, it highly depends on the accurate forecast of hydrogen consumption. In this paper, we propose a deep leaning based forecasting approach for predicting hydrogen consumption of fuel cell vehicl…
Study shows climate change can cause a 'run on fossil fuels' affecting prices and production.
In this paper, we consider the problem of reducing the bit error rate of flash-based solid state drives (SSDs) when cells are subject to inter-cell interference (ICI). By observing that the outputs of adjacent victim cells can be correlated due to common aggressors, we propose a novel channel model to accurately repres…
Study builds ML models to predict fuel properties accurately.
Airlines optimize fuel loading with better flight time predictions.
Real-time fuel leakage detection framework MOCPD improves accuracy.
Zero Emission Vehicles (ZEV) play an important role in the decarbonization of the transportation sector. For a wider adoption of ZEVs, providing a reliable infrastructure is critical. We present a machine learning approach that uses unsupervised temporal clustering algorithm along with survey analysis to determine infr…
New indicator detects financial strain through smart meter data.
Study on AI-driven modeling for high burnup accident-tolerant fuels in SMRs.
Identifying important components or factors in large amounts of noisy data is a key problem in machine learning and data mining. Motivated by a pattern decomposition problem in materials discovery, aimed at discovering new materials for renewable energy, e.g. for fuel and solar cells, we introduce CombiFD, a framework …
Paper develops robust neural network sensors for fuel injection quantities.
Custom loss functions improve accuracy of wildfire rate of spread forecasts.
This research assesses uncertainty quantification and sensitivity analysis for DTs in nuclear fuel performance.
Space2Vec learns multi-scale spatial representations from grid cell insights.
The need for advanced materials has led to the development of complex, multi-component alloys or solid-solution alloys. These materials have shown exceptional properties like strength, toughness, ductility, electrical and electronic properties. Current development of such material systems are hindered by expensive expe…
A method for quickly determining deployment schedules that meet a given fuel cycle demand is presented here. This algorithm is fast enough to perform in situ within low-fidelity fuel cycle simulators. It uses Gaussian process regression models to predict the production curve as a function of time and the number of depl…
Optimal engine operation during a transient driving cycle is the key to achieving greater fuel economy, engine efficiency, and reduced emissions. In order to achieve continuously optimal engine operation, engine calibration methods use a combination of static correlations obtained from dynamometer tests for steady-stat…
We introduce a new and highly tractable structural model for spot and derivative prices in electricity markets. Using a stochastic model of the bid stack, we translate the demand for power and the prices of generating fuels into electricity spot prices. The stack structure allows for a range of generator efficiencies p…
Estimates reliability of nuclear fuel using advanced modeling techniques.
We introduce two Python frameworks to train neural networks on large datasets: Blocks and Fuel. Blocks is based on Theano, a linear algebra compiler with CUDA-support. It facilitates the training of complex neural network models by providing parametrized Theano operations, attaching metadata to Theano's symbolic comput…
Study assesses data-driven and physics-based SGS models for transcritical combustion.
In April 2009, we introduced a model representing the evolution of motor fuel price (a subcategory of the consumer price index of transportation) relative to the overall CPI as a linear function of time. Under our framework, all price deviations from the linear trend are transient and the price must promptly return to …
Conversion of corn to ethanol in the US since 2005 has been a major cause of global food price increases during that time and has been shown to be ineffective in achieving US energy independence and reducing environmental impact. We make three key statements to enhance understanding and communication about ethanol prod…
Identifies Heegaard Floer homology solid tori via Dehn fillings.
We solve a class of control problems with fuel constraint by means of the log-Laplace transforms of -functionals of Dawson-Watanabe superprocesses. This solution is related to the superprocess solution of quasilinear parabolic PDEs with singular terminal condition. For the probabilistic verification proof, we develo…
To achieve the ambitious aims of the Paris climate agreement, the majority of fossil-fuel reserves needs to remain underground. As current national government commitments to mitigate greenhouse gas emissions are insufficient by far, actors such as institutional and private investors and the social movement on divestmen…
We propose a new structural model that can compute the electricity spot and forward prices in two coupled markets with limited interconnection and multiple fuels. We choose a structural approach in order to represent some key characteristics of electricity spot prices such as their link to fuel prices, consumption leve…
Proves NP and co-NP status for knot core recognition in solid torus.
New theorem for 4D links simplifies characterisation problem.
We consider the solid angle that a planar compact subset subtends at a point in a level set of height h and study two extremal problems for the solid angle. One of the variables is a point in such a plane, that is, we study the properties of the solid angle maximizer. The other is the pair of a planar compact subset an…
We consider a semilinear parabolic degenerated Hamilton-Jacobi-Bellman (HJB) equation with singularity which is related to a stochastic control problem with fuel constraint. The fuel constraint translates into a singular initial condition for the HJB equation. We first propose a transformation based on a change of vari…
Classifies small links in an unmarked solid torus.
New method constructs Seifert solids from bridge trisections.
We show that in any triangulation of a solid torus, there is a pre-core curve that lies in the 2-skeleton and that intersects the interior of each face in at most 10 straight arcs. By definition, a pre-core curve is a simple closed curve that becomes a core curve when a collar is attached to the boundary of the solid t…
The paper classifies all tight contact structures on a solid torus.
Small deformations of a specific type of Lorentzian space-time preserve its structure.
This paper has been withdrawn by the author due to some inaccurate descriptions in the section of INTRODUCTION and CONCLUSIONS.
Study Murasugi sum in 4D for knotted surfaces, defining arborescent surfaces.
Geometrically reformulates Cosserat solid mechanics using differential geometry.
The paper introduces surfaces with constant solid angle for designing shell structures.