arXiv research

Study electric field and potential of torus knots, focusing on z-axis.

Optimizes electric field to control molecule states in Hartree-Fock theory.

Study predicts turbulent electric fields in fusion plasmas using deep learning.

Neural nets solve electric field in non-convex microfluidic devices.

This article provides some estimates for the relative sizes of the electric and magnetic contributions to the energy functional for the minimum energy configuration of an SU(2) gauge field on R^3 in the presence of an source in a fixed ball. The surprising fact is that the contribution to both energies from the free fi…

Stable knots and links can exist in electromagnetic fields.

Optimal trading strategies identified in electricity markets with a major player.

The Lorentz force equations provide a partial description of the geodesic motion of a charged particle on a four-manifold. Under the hypothesis that Maxwell's equations express symmetry properties of the Ricci tensor, the full electromagnetic connection is determined. From this connection, the fourth equation of the ge…

Develops a new model for day-ahead electricity prices using ambit fields.

The paper uses DNN for electricity price forecasting and XAI for understanding the factors.

Modeling price formation in intraday electricity markets with renewable generation.

Electrostatic systems with specific tensors are locally conformally flat.

Energy price forecasting is a relevant yet hard task in the field of multi-step time series forecasting. In this paper we compare a well-known and established method, ARMA with exogenous variables with a relatively new technique Gradient Boosting Regression. The method was tested on data from Global Energy Forecasting …

Graph neural networks predict solid-state NMR parameters from atomic structures.

In this paper, we prove conformal positive mass theorems for asymptotically flat manifolds with charge. We apply conformal relations to show that if the conformal sum of scalar curvature is not less than the norm square of electric field and electric density, the sum of the mass will not less than the modulus of total …

The study connects electromagnetic structures to Legendrian fields on the 3-sphere.

In Electricity markets, illiquidity, transaction costs and market price characteristics prevent managers to replicate exactly contracts. A residual risk is always present and the hedging strategy depends on a risk criterion chosen. We present an algorithm to hedge a position for a mean variance criterion taking into ac…

Study spherical doubly warped spacetimes for stellar collapse and cosmology.

Kernel-based mean-field games use MMD penalties for interaction and target costs.

In the paper [4] is presented a theory which unifies the gravitation theory and the mechanical effects, which is different from the Riemannian theories like GTR. Moreover it is built in the style of the electomagnetic field theory. This paper is a continuation of [4] such that the complex variant of that theory yields …

Adaptive probabilistic load forecasting improves performance in power systems.

Transfer learning improves electricity price forecasting accuracy.

Deep brain stimulation (DBS) is a surgical treatment for Parkinson's Disease. Static models based on quasi-static approximation are common approaches for DBS modeling. While this simplification has been validated for bioelectric sources, its application to rapid stimulation pulses, which contain more high-frequency pow…

Machine learning improves electricity price forecasting.

The Dirac equation for massive free electrically neutral spin 1/2 particles in a gravitation field is considered. The secondary quantization procedure is applied to it and the Hilbert space of multiparticle quantum states is constructed.

Model simulates sparse order books in illiquid markets.

The development of personalized human head models from medical images has become an important topic in the electromagnetic dosimetry field, including the optimization of electrostimulation, safety assessments, etc. Human head models are commonly generated via the segmentation of magnetic resonance images into different…

THieF improves day-ahead electricity price prediction accuracy by reconciling hourly and block forecasts.

Study magnetic potentials on Anosov manifolds using spectral data.

Neural networks outperform single-hour models in day-ahead electricity price forecasting.

Study rigidifies geometry of electrostatic systems with specific tensor properties.

For a compact, connected, oriented Riemannian $3$-manifold $(M, g)$ with smooth boundary $\partial M$, we explicitly give a local representation and a full symbol expression for the electromagnetic Dirichlet-to-Neumann map by factorizing Maxwell's equations and using an isometric transform. We prove that one can recons…

The paper forecasts joint electricity demand across 14 British regions using additive models.

ARHNN method improves electricity price forecasting accuracy.

Method learns molecular Hamiltonian for accurate electron dynamics predictions.

The paper evaluates and benchmarks electricity price forecasting models.

We consider a magnetic Schrödinger operator $H^h$, depending on a semiclassical parameter $h>0$, on a compact Riemannian manifold. We assume that there is no electric field. We suppose that the minimal value $b_0$ of the intensity of the magnetic field $b$ is strictly positive. We give a survey of the results on asympt…

Due to the liberalization of markets, the change in the energy mix and the surrounding energy laws, electricity research is a dynamically altering field with steadily changing challenges. One challenge especially for investment decisions is to provide reliable short to mid-term forecasts despite high variation in the t…

Let A be the space of irreducible connections (vector potentials) over a SU(n)-principal bundle on a three-dimensional manifold M. Let T be the fiber product of the tangent and cotangent bundles of A. We endow T with a symplectic structure Ωwhich is represented by a vortex formula. The corresponding Poisson bracket wil…

We construct a covariant functor from a category of Abelian principal bundles over globally hyperbolic spacetimes to a category of *-algebras that describes quantized principal connections. We work within an appropriate differential geometric setting by using the bundle of connections and we study the full gauge group,…

Reduces field theories using Poisson-Poincaré method.

We examine the novel problem of the estimation of transaction arrival processes in the intraday electricity markets. We model the inter-arrivals using multiple time-varying parametric densities based on the generalized F distribution estimated by maximum likelihood. We analyse both the in-sample characteristics and the…

Deep learning solves EV routing with time windows for EV fleets.

Introduces a new tensor for electrostatic systems in arbitrary dimensions.

Electromagnetic stimulation of the human brain is a key tool for the neurophysiological characterization and diagnosis of several neurological disorders. Transcranial magnetic stimulation (TMS) is one procedure that is commonly used clinically. However, personalized TMS requires a pipeline for accurate head model gener…

It is very vital for suppliers and distributors to predict the deregulated electricity prices for creating their bidding strategies in the competitive market area. Pre requirement of succeeding in this field, accurate and suitable electricity tariff price forecasting tools are needed. In the presence of effective forec…

Management and efficient operations in critical infrastructure such as Smart Grids take huge advantage of accurate power load forecasting which, due to its nonlinear nature, remains a challenging task. Recently, deep learning has emerged in the machine learning field achieving impressive performance in a vast range of …

Electrostatics method samples complex distributions deterministically.