Power and thermal management are critical components of High-Performance-Computing (HPC) systems, due to their high power density and large total power consumption. The assessment of thermal dissipation by means of compact models directly from the thermal response of the final device enables more robust and precise the…
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48 results for “Thermal systems”
ARX models predict thermal behavior of WBG semiconductors accurately.
problem Thermal management challenges of WBG semiconductors.
method Use of ARX parametric models based on experimental measurements.
result ARX models provide accurate temperature predictions without detailed component information.
Paper addresses LSTM stability for thermal systems using infinity-norm.
problem Stability of LSTM networks in thermal systems.
method Derived ISS condition for LSTM, developed training strategy.
result ISS-promoted LSTM outperforms other models in thermal system case study.
Wealth Thermalization Hypothesis and Social Networkscond-mat.stat-mech
The study applies wealth thermalization hypothesis to social networks and explains inequality.
problem Explains inequality in human society through wealth thermalization hypothesis.
method Uses Random Matrix Theory and social networks with nonlinear perturbation.
result Shows that wealth distribution follows Rayleigh-Jeans distribution, leading to inequality.
Thermalizer stabilizes autoregressive models for long-term predictions in chaotic systems.
problem Long-term predictions in chaotic spatiotemporal systems are unreliable due to trajectory divergence.
method Diffusion models are used to implicitly estimate the score of an invariant measure, which stabilizes autoregressive emulators by applying denoising during inference.
result Thermalization extends the time horizon of stable predictions by an order of magnitude in chaotic systems.
The large thermal capacity of buildings enables heating, ventilating, and air-conditioning (HVAC) systems to be exploited as demand response (DR) resources. Optimal DR of HVAC units is challenging, particularly for multi-zone buildings, because this requires detailed physics-based models of zonal temperature variations…
Dynamical thermalization and turbulence in social stratification modelscond-mat.stat-mech
Study chaotic dynamics in social stratification models leading to thermalization and turbulence.
problem Understanding social stratification dynamics through chaotic nonlinear systems.
method Modeling social network links with oscillators and energies, studying Hamiltonian evolution and nonlinear interactions.
result Chaotic dynamics leads to dynamical thermalization and Kolmogorov-Zakharov turbulence, with implications for wealth inequality.
The study investigates noise effects on parameter estimation for Ornstein-Uhlenbeck processes.
problem Impact of noise on parameter fitting for Ornstein-Uhlenbeck processes.
method Proposed algorithms to distinguish between thermal and multiplicative noise.
result Effective methods to estimate parameters even when multiplicative noise dominates.
Paper uses deep learning to improve thermal-hydraulic simulations.
problem Limited credibility of thermal-hydraulic codes in real plant conditions.
method Feature Similarity Measurement (FSM) and deep learning.
result Deep learning constructs relationships between local physical features and simulation errors.
Deep learning adapts HVAC models to new buildings.
problem Adapting thermal dynamics models to new buildings with limited data.
method Deep supervised domain adaptation (DSDA) using LSTM-based Sequence to Sequence model.
result Deep supervised domain adaptation improves predictive performance over learning from scratch.
This work generates synthetic 3D thermal facial data using 2D facial data and deep learning.
problem Creating large datasets for deep learning in computer vision.
method 3D facial modelling techniques and deep learning methodologies.
result Synthetic 3D thermal facial data created for deep learning applications.
New method predicts heat load in thermal grids using latent variables.
problem Predicting heat load in district energy systems.
method Combines nominal model for outdoor temperature with latent variable model for residual heat load.
result Proposed method achieves better prediction accuracy than artificial neural networks.
Kinetic Economiesq-fin.GN
We study a minimalist kinetic model for economies. A system of agents with local trading rules display emergent demand behaviour. We examine the resulting wealth distribution to look for non-thermal behaviour. We compare and contrast this model with other similar models.
Meta-learning algorithms prepare quantum Gibbs states efficiently for NISQ devices.
problem Efficiently preparing quantum Gibbs states for NISQ devices.
method Meta-Variational Quantum Thermalizer (Meta-VQT) and Neural Network Meta-VQT (NN-Meta VQT) algorithms.
result Meta-learned parameters significantly outperform random initializations in optimization tasks.
Study on uniquely determining thermal properties from boundary temperature and heat flux measurements.
problem Determine thermal conductivity and volumetric heat capacity from boundary measurements.
method Uniqueness proof for isotropic and anisotropic media under thermal diffusivity assumption.
result Uniqueness of thermal properties in all dimensions and up to a gauge in two dimensions.
Thermodynamic theory of voting and EU electionscond-mat.stat-mech
A thermodynamic theory explains EU election vote distributions.
problem Describing EU election vote distributions.
method Tracing parallels between system energies of coupled nonlinear oscillators and party vote fractions.
result The Rayleigh-Jeans (RJ) theory well depicts EU vote results and candidate vote dispersion.
With the fast growth in the visual surveillance and security sectors, thermal infrared images have become increasingly necessary ina large variety of industrial applications. This is true even though IR sensors are still more expensive than their RGB counterpart having the same resolution. In this paper, we propose a d…
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraintscs.LG
Thermal preferences vary from person to person and may change over time. The main objective of this paper is to sequentially pose intelligent queries to occupants in order to optimally learn the indoor air temperature values which maximize their satisfaction. Our central hypothesis is that an occupant's preference rela…
Learning influence pathways of a network of dynamically related processes from observations is of considerable importance in many disciplines. In this article, influence networks of agents which interact dynamically via linear dependencies are considered. An algorithm for the reconstruction of the topology of interacti…
In the emerging advancement in the branch of autonomous robotics, the ability of a robot to efficiently localize and construct maps of its surrounding is crucial. This paper deals with utilizing thermal-infrared cameras, as opposed to conventional cameras as the primary sensor to capture images of the robot's surroundi…
Generative Thermal Design Through Boundary Representation and Multi-Agent Cooperative Environmentcs.LG
Generative thermal design learns optimal shapes using multi-agent reinforcement learning.
problem Complex thermal design challenges due to convection-diffusion equation and boundary interactions.
method Cooperative multi-agent deep reinforcement learning with continuous geometric representation.
result Framework learns optimal design strategies without shape derivation or differentiable objectives.
A modified GAN improves thermal comfort classification models by balancing imbalanced datasets.
problem Imbalanced thermal comfort datasets make it hard to train accurate models.
method Proposed a modified conditional GAN (comfortGAN) to balance the dataset.
result A balanced dataset trained with comfortGAN yields higher classification accuracy.
Study finds almost contact structures in thermal QCD-like theories at intermediate coupling.
problem Understanding (Almost) Contact Structures in thermal QCD-like theories.
method Explicitly obtained (Almost) Contact Structures and SU(3) structures.
result Subspaces of C3S and AC3S are not mutually 'N-path connected' in the Infra-Red.
The goal of this paper is to assess the utility of Reduced-Order Models (ROMs) developed from 3D physics-based models for predicting transient thermal power output for an enhanced geothermal reservoir while explicitly accounting for uncertainties in the subsurface system and site-specific details. Numerical simulations…
CNN accurately reconstructs lattice topology with strong thermal fluctuations.
problem Reconstructing lattice topology with strong thermal fluctuations and unbalanced data.
method Deep convolutional neural network (CNN) mapping local magnetic moments to coupling probabilities.
result CNN accurately reconstructs lattice topology where thermal fluctuations dominate.
Thermodynamic description of world GDP distribution over countriescond-mat.stat-mech
The world GDP distribution is described using thermodynamics principles.
problem Understanding the distribution of GDP among countries.
method Applied Rayleigh-Jeans thermalization to GDP data.
result Emergence of low GDP states similar to condensation in optics.
Method infers causal structure from system behaviors using RKHS and kernel -machines.
problem Discovering causal structure in systems with varying external and measurement noise.
method Combines causal states and RKHS for efficient representation and inference of causal structure.
result Robustly estimates causal structure in high-dimensional data with varying noise.
New deep learning model optimizes energy use in buildings.
problem Optimizing energy use and comfort in large buildings.
method Transformer-based metamodel trained with simulation and sensor data, calibrated with CMA-ES, optimized with multi-objective algorithms.
result Optimal settings reduce energy loads while maintaining thermal comfort and air quality.
The thermal subsystem of the Mars Express (MEX) spacecraft keeps the on-board equipment within its pre-defined operating temperatures range. To plan and optimize the scientific operations of MEX, its operators need to estimate in advance, as accurately as possible, the power consumption of the thermal subsystem. The re…
Paper uses VAEs to detect radar targets in complex noise.
problem Detecting radar targets in compound clutter and thermal noise.
method Proposes a VAE architecture to distinguish radar targets from various noise types.
result The VAE outperforms classical detectors in challenging noise conditions.
Study of -theory dual of thermal QCD-like theories at intermediate coupling.
problem Missing top-down holographic dual for thermal QCD-like theories at intermediate 't Hooft coupling.
method Analysis of corrections and corrections in the MQGP background.
result Discovery of corrections and -structure classification of underlying geometries.
On curved spaces, viscous fluids reach equilibrium quickly.
problem Thermalization of viscous fluids on negatively curved manifolds.
method Stochastic Navier-Stokes equations with kinematically selected deformation Laplacian.
result Exponential thermalization rate of $2νλ_\Def$.
Temporal evolution of the "thermal" and "superthermal" income classes in the USA during 1983-2001cond-mat.other
Personal income distribution in the USA has a well-defined two-class structure. The majority of population (97-99%) belongs to the lower class characterized by the exponential Boltzmann-Gibbs ("thermal") distribution, whereas the upper class (1-3% of population) has a Pareto power-law ("superthermal") distribution. By …
This paper optimizes a power-to-heat system using reinforcement learning for cost minimization under uncertain conditions.
problem Optimizing a power-to-heat system with fluctuating renewable energy sources.
method Stochastic optimal control, reinforcement learning (Q-learning).
result Reinforcement learning provides an efficient solution to the optimization problem.
Length and torsion dependence of thermal conductivity in twisted graphene nanoribbonscond-mat.mes-hall
Study examines how twisting graphene nanoribbons affects their thermal conductivity.
problem Understanding how twisting affects thermal conductivity in graphene nanoribbons.
method Calculated geometric parameters of TGNRs, including twist and writhe, and used molecular dynamics simulations.
result Twisted graphene nanoribbons require at least two parameters to accurately describe their thermal conductivity.
As energy markets begin clearing at sub-hourly rates, their interaction with load control systems becomes a potentially important consideration. A simple model for the control of thermal systems using market-based power distribution strategies is proposed, with particular attention to the behavior and dynamics of elect…
Improved person detection in occluded conditions with AOS images.
problem Inaccurate classification of partially occluded people, animals, or objects.
method Combining multi-perspective images captured by Airborne Optical Sectioning (AOS) to achieve better precision and recall.
result Precision/recall of 96/93% for automated person detection.
An innovative method optimizes engine calibration to improve efficiency and reduce emissions.
problem Complex engines with many tunable parameters require efficient calibration methods.
method Combines Principal Component Decomposition with constrained Bayesian Optimization to minimize pressure curve deviation.
result Optimal engine calibration found after 64.4s with a 0.017% efficiency gain.
The paper optimizes air conditioning setpoints using machine learning.
problem Static setpoints waste energy and increase costs.
method Deep-learning model based on RNNs for predicting future temperatures.
result RNNs outperform state-of-the-art models in prediction accuracy.
Statistical mechanics of moneycond-mat.stat-mech
In a closed economic system, money is conserved. Thus, by analogy with energy, the equilibrium probability distribution of money must follow the exponential Gibbs law characterized by an effective temperature equal to the average amount of money per economic agent. We demonstrate how the Gibbs distribution emerges in c…
We present an agent behavior based microscopic model that induces jumps, spikes and high volatility phases in the price process of a traded asset. We transfer dynamics of thermally activated jumps of an unexcited/ excited two state system discussed in the context of quantum mechanics to agent socio-economic behavior an…
In this letter, we apply the artificial neural network in a supervised manner to map out the quantum phase diagram of disordered topological superconductor in class DIII. Given the disorder that keeps the discrete symmetries of the ensemble as a whole, translational symmetry which is broken in the quasiparticle distrib…
A popular approach for predicting the future of dynamical systems involves mapping them into a lower-dimensional "latent space" where prediction is easier. We show that the information-theoretically optimal approach uses different mappings for present and future, in contrast to state-of-the-art machine-learning approac…
Young isolated neutron stars (INS) most commonly manifest themselves as rotationally powered pulsars (RPPs) which involve conventional radio pulsars as well as gamma-ray pulsars (GRPs) and rotating radio transients (RRATs). Some other young INS families manifest themselves as anomalous X-ray pulsars (AXPs) and soft gam…
New MBL hidden Born machine learns various tasks.
problem Learning from quantum many-body systems.
method MBL dynamics and hidden units for training.
result Enhanced trainability and stability in learning.
New formalism solves kinematical constraints in curved backgrounds and non-trivial states.
problem Solving kinematical constraints due to Weyl invariance in curved backgrounds and non-trivial states.
method Constructing Weyl covariant geometric objects and identifying them as building blocks of correlation functions.
result Exact agreement with thermal OPEs and holographic computations for thermal 2-point functions.
Study examines how economic policy uncertainty impacts stock markets.
problem Dynamic relationship between economic policy uncertainty and stock markets.
method Used symmetric thermal optimal path (TOPS) method.
result Different interaction patterns observed in emerging and developed markets.
Generalizes embedding formalism for CFTs on curved backgrounds.
problem Capturing CFTs on curved backgrounds and non-trivial states.
method Using ambient metric and geometric invariants of the ambient space.
result Exact agreement with holographic computations and thermal OPEs.