Study electric field and potential of torus knots, focusing on z-axis.
problem Analyze electric field and potential of torus knots.
method Parametrize torus knots, use symmetry, numerical methods, contour integration.
result Electric field is zero only at the origin, extreme points analyzed.
Study predicts turbulent electric fields in fusion plasmas using deep learning.
problem Predicting turbulent electric fields in fusion plasmas.
method Physics-informed deep learning, drift-reduced Braginskii theory, experimental data.
result Neutrals broaden turbulent field amplitudes and increase shearing rates.
Proposes a new model for DBS electric field dynamics.
problem Inaccurate static models for DBS therapeutic results.
method Hybrid approach using Gaussian processes and wave equation, avoiding quasi-static approximation.
result Model accurately describes dynamic behavior of DBS and solves inverse problems.
Optimal trading strategies identified in electricity markets with a major player.
problem Price formation and optimal trading in intraday electricity markets with strategic interactions.
method Stochastic control theory and mean field games with a major player.
result Nash equilibrium identified in closed form for the asymptotic case.
Develops a new model for day-ahead electricity prices using ambit fields.
problem The high-dimensional panel structure of electricity spot prices in European zones.
method Formulates a continuous time framework as an ambit field indexed by a cylinder surface, embedding intrinsic dependence structures.
result The model allows for pricing of derivatives on individual delivery periods, making products like spreads analytically tractable.
Neural nets solve electric field in non-convex microfluidic devices.
problem Solving differential equations in non-convex geometries.
method Neural network approximation of electric potential and field.
result Deep neural networks outperform shallow networks in accuracy.
Modeling price formation in intraday electricity markets with renewable generation.
problem Price formation and optimal trading strategies in intraday electricity markets with intermittent renewable generation.
method Developed a tractable equilibrium model using stochastic control theory to identify optimal strategies and exhibit Nash equilibrium.
result Identified optimal trading strategies and exhibited Nash equilibrium in closed form for a finite number of agents and in the asymptotic framework of mean field games.
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.
problem Stability of knots and links in electromagnetic fields.
method Proving the existence of electromagnetic fields preserving link topology.
result Every link can be realized as stable field lines in electromagnetic fields.
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…
Transfer learning improves electricity price forecasting accuracy.
problem Accurate day-ahead electricity price prediction using available data.
method Pre-train a neural network on source markets and fine-tune for target market.
result Transfer learning significantly improves forecasting performance.
Algorithm optimizes hedging in electricity markets by minimizing variance risk.
problem Hedging contracts in electricity markets due to liquidity and market characteristics.
method Developed an algorithm for mean variance hedging considering transaction costs and market depth.
result Algorithm effectively reduces residual risk in electricity market positions.
The paper proves a new theorem linking mass and electric charge for certain types of manifolds.
problem Proving a new positive mass theorem for manifolds with charge.
method Using conformal relations and scalar curvature, the authors derive a new theorem.
result The sum of mass is not less than the modulus of total electric charge under certain conditions.
Machine learning improves electricity price forecasting.
problem Predicting electricity prices in various horizons.
method Application of machine learning techniques to EPF models.
result Machine learning models outperform traditional methods.
Model simulates sparse order books in illiquid markets.
problem Inaccurate LOB models in illiquid markets.
method Inhomogeneous Poisson process for order arrivals and cancellations.
result Enhanced understanding of LOB dynamics in illiquid markets.
The study connects electromagnetic structures to Legendrian fields on the 3-sphere.
problem Understanding the topology of stable electromagnetic structures.
method Connecting null solutions to Maxwell's equations with Legendrian fields on the 3-sphere.
result Any (possibly knotted) toroidal surface can be realized as a magnetic surface of a null solution, implying stability.
The paper improves electricity price forecasting using future prices.
problem Reliable short to mid-term forecasts of electricity prices despite high variation.
method Combining econometric autoregressive models with future prices for improved forecasting performance.
result The model can outperform other models in the literature and maintain hourly precision.
Neural networks outperform single-hour models in day-ahead electricity price forecasting.
problem Improving accuracy in day-ahead electricity price forecasting.
method Compared two neural network structures: one-hour models and daily auction models.
result Daily auction models outperform one-hour models in forecasting accuracy.
New deep learning model generates accurate personalized human head models for electromagnetic dosimetry.
problem Challenges in generating accurate human head models for personalized electromagnetic dosimetry.
method Proposed ForkNet architecture for segmentation of whole human head structures using deep learning.
result Generated head models exhibit strong matching with manual segmentation results.
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 …
Optimizes electric field to control molecule states in Hartree-Fock theory.
problem Optimizing electric field to drive molecule from initial to target state.
method Trust region optimization with gradients from adjoint state method.
result Achieves desired target states with minimal control effort.
Deep learning models improve electric load forecasting accuracy.
problem Accurate short-term electric load forecasting remains challenging.
method Comprehensive evaluation of various deep learning architectures on real-world datasets.
result Deep learning models outperform traditional methods in electric load forecasting.
Estimates transaction arrival patterns in intraday electricity markets.
problem Estimating transaction arrival processes in intraday electricity markets.
method Model inter-arrivals using multiple time-varying parametric densities based on the generalized F distribution.
result Significant insights into model fit and prediction accuracy evaluated by various metrics.
The paper evaluates and benchmarks electricity price forecasting models.
problem Lack of rigorous evaluation methods and open datasets.
method Literature review, cross-market comparison, open datasets, and python toolbox.
result Best practices for electricity price forecasting are proposed.
The study compares various electricity tariff price forecasting techniques in Turkey.
problem Accurate prediction of electricity tariffs for market success.
method Comparison of nine forecasting models based on MAPE, MAD, and MSD.
result Best models provided good forecasts for electricity tariff periods.
Method learns molecular Hamiltonian for accurate electron dynamics predictions.
problem Predict electron dynamics in molecules using learned Hamiltonians.
method Combines linear statistical model with quantum Liouville equation time discretization.
result Predicted electron dynamics closely matches ground truth, even beyond training data.
ARHNN method improves electricity price forecasting accuracy.
problem Improving accuracy in electricity price forecasting.
method Combines Autoregressive Hybrid Nearest Neighbors (ARHNN) method with calibration sample selection and forecast combination.
result ARHNN method outperforms benchmarks by up to 10% in German, Spanish, and New England markets.
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.
CP provides reliable prediction intervals for short-term power markets.
problem Short-term electricity price forecasting in power markets.
method Conformal Prediction (CP) integrated with various point forecast models.
result CP yields sharp and reliable prediction intervals in short-term power markets.
Study magnetic potentials on Anosov manifolds using spectral data.
problem Recover magnetic potentials from spectral data on Anosov manifolds.
method Utilize principal wave trace invariants and magnetic Schrödinger operator.
result Spectral data uniquely determines magnetic and electric potentials on Anosov manifolds.
Reduces field theories using Poisson-Poincaré method.
problem Reduction of field theories using Poisson-Poincaré method.
method Poisson-Poincaré reduction for field theories.
result Reduction procedure for field theories.
Paper proposes a neural network for estimating brain conductivity without segmentation.
problem Accurate head model generation for personalized TMS with realistic conductivity.
method Convolutional neural network estimating conductivity from MRI data.
result Smooth electric field results similar to conventional methods without segmentation.
Model predicts climate-sensitive water and electricity use in Midwestern cities.
problem Ensuring conservation measures in growing cities under climate change.
method Statistical learning theory-based modeling framework for predicting climate-sensitive water-electricity demand nexus.
result Water use is slightly more sensitive to climate than electricity use.
Reconstructing 3D manifolds from boundary electromagnetic data.
problem Reconstructing a compact, connected, real-analytic Riemannian 3-manifold from tangential electric and magnetic fields on its boundary.
method Factorizing Maxwell's equations and using an isometric transform to reconstruct the metric.
result The electromagnetic Dirichlet-to-Neumann map uniquely determines all derivatives of electromagnetic parameters on the boundary.
Study spherical doubly warped spacetimes for stellar collapse and cosmology.
problem Analyzing spherically symmetric spacetimes for stellar collapse and cosmology.
method Obtained results for Weyl and Ricci tensors on general doubly warped spacetimes.
result Friedmann equations deviate from standard FRW cosmology due to electric tensor terms.
Abstract discusses symplectic structure and Maxwell equations on Yang-Mills fields.
problem Formulating Maxwell equations on Yang-Mills fields using symplectic structures.
method Endowed a symplectic structure on the fiber product of tangent and cotangent bundles of connections, derived Hamiltonian equations and moment maps.
result Proved Maxwell equations and derived new conserved quantities from moment maps.
New model improves field learning with improved equivariance.
problem Learning equivariant stochastic fields.
method Equivariant Gaussian processes and Steerable Conditional Neural Processes.
result SteerCNPs significantly improve performance in transfer learning tasks.
We consider a magnetic Schrödinger operator Hh, 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 b0 of the intensity of the magnetic field b is strictly positive. We give a survey of the results on asympt…
PSQRNN model forecasts electricity consumption in China by integrating neural networks and quantile regression.
problem Electricity forecasting in China due to regional economic, social, and natural conditions.
method PSQRNN combines neural networks and semiparametric quantile regression to model electricity consumption.
result PSQRNN model outperforms traditional methods in forecasting electricity consumption in China.
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,…
New approach predicts electricity prices for months to years with probabilistic forecasts.
problem Uncertainty in long-term electricity price forecasting.
method Extends X-Model using supply and demand curve for hourly electricity prices.
result Probabilistic forecasts detect long-term price spikes.
Physics-informed ML models improve turbulence understanding in fusion plasmas.
problem Improving turbulence modeling in fusion plasma devices.
method Physics-informed deep learning framework constrained by PDEs.
result Direct quantitative comparisons of turbulent fields between theory and gyrokinetic models.
Deep neural networks predict electricity consumption accurately.
problem Predicting future electricity consumption for better management.
method Used Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) networks to predict electricity consumption based on past data.
result Both RNN and LSTM achieved an average Root Mean Square error of 0.1.
Study predicts electricity prices using LSTM models with feature selection, considering market coupling.
problem Accurate day-ahead electricity price forecasting in coupled markets.
method Hybrid LSTM-based deep learning models with feature selection algorithms.
result Proposed models achieve considerably accurate results in Nordic market.
How does dynamic price information flow among Northern European electricity spot prices and prices of major electricity generation fuel sources? We use time series models combined with new advances in causal inference to answer these questions. Applying our methods to weekly Nordic and German electricity prices, and oi…
Transformer model forecasts electricity price spread for virtual bidding.
problem Volatility in renewable energy causes price forecasting challenges.
method Transformer-based deep learning model using various time-series features.
result Trading strategy at peak hour yields nearly consistent profit.
Paper models and forecasts intra-day electricity price spreads.
problem Forecasting intra-day price spreads for electricity traders and operators.
method Dynamic density functions based on skewed-t distributions, conditional on exogenous drivers.
result Best fitting and forecasting specifications selected using Pinball Loss function.
ElecSim models long-term electricity planning with agent-based Monte-Carlo simulations.
problem Transitioning to zero-carbon energy systems requires careful policy decisions.
method Agent-based Monte-Carlo model for long-term electricity investment decisions.
result Monte-Carlo simulation improves model performance by 52.5%.