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.
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.
This paper proposes a deep learning method to improve thermal image resolution.
problem Improving the resolution of thermal infrared images.
method Integrates high-frequency information from visual images to enhance thermal image resolution.
result The proposed multimodal fusion model outperforms state-of-the-art methods in super-resolution.
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.
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.
Paper develops machine learning methods to identify thermal models for HPC clusters.
problem Accurate thermal modeling for high-power HPC systems with diverse workloads.
method Advanced system identification algorithm combined with machine learning for data selection.
result Very accurate thermal models generated for HPC systems (average error < 1°C).
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.
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.
Machine learning predicts Mars Express spacecraft's thermal power consumption.
problem Accurately predict Mars Express spacecraft's thermal power consumption for optimal operation planning.
method State-of-the-art feature engineering and machine learning methods.
result The proposed pipeline improves predictive performance and efficiency.
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.
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.
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. The paper learns personalized thermal preferences using Bayesian active learning.
problem Learning personalized thermal preferences from occupant feedback.
method Bayesian active learning with unimodality constraints on Gaussian process.
result The method requires fewer observations to learn optimal temperature preferences.
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.
Paper proposes an edge detection method for robot navigation using low-SNR thermal cameras.
problem Efficient edge detection for robot navigation using low-SNR thermal camera.
method Raw image denoising, Canny edge detection, CSS method, edge ranking, edge linking.
result Enhanced edge detection method effectively detects smooth edges of the surrounding environment.
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.
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 $2νλ_\Def$.
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 M-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 O(R4) corrections and O(lp6) corrections in the MQGP background. result Discovery of O(R4) corrections and G-structure classification of underlying geometries. A new SL strategy optimizes HVAC DR in multi-zone buildings.
problem Optimal DR of HVAC units in multi-zone buildings is challenging.
method Supervised learning with ANN replication and DNN integration.
result SLAMP achieves effective DR schedules with reduced computation time.
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 …
Paper models buildings' thermal characteristics with a Bayesian approach.
problem Modeling buildings' heat dynamics with various factors.
method Bayesian state-space model incorporating prior knowledge.
result Bayesian approach provides similar parameters as MCMC but faster.
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.
NMF identifies hidden component processes from thermal manufacturing data.
problem Thermal manufacturing processes with many interacting parameters are hard to diagnose.
method Non-negative matrix factorization guided by a knowledge-based initialization strategy.
result Identifies physical meaningful sources from temperature time series.
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.
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.
Machine learning helps dispatchers manage power grids more efficiently.
problem Managing power grids with varying demands and complex production systems.
method Developed novel machine learning techniques to mimic human decisions and devise remedial actions.
result The approach successfully prevents power flow limits violations in real-time.
In the theory of riskfree hedges in continuous time finance, one can start with the delta-hedge and derive the option pricing equation, or one can start with the replicating, self-financing hedging strategy and derive both the delta-hedge and the option pricing partial differential equation. Approximately reversible tr…
Artificial neural networks map quantum phases of disordered topological superconductors.
problem Classifying quantum phases of disordered topological superconductors.
method Supervised artificial neural network trained on ensemble averages of quasiparticle distributions.
result Artificial neural networks can classify quantum phases with high confidence, identifying unknown phases.
New method ranks power grid contingencies for faster security assessment.
problem Maintain high voltage power transmission networks in security.
method Neural network-based ranking of higher order contingencies.
result Residual risk of contingencies decreases dramatically compared to considering only N-1 cases.
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.
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…
Quantum algorithm approximates Khovanov homology ranks.
problem Efficient computation of Khovanov homology ranks.
method Novel quantum algorithm with pre-thermalization procedure.
result Additive approximations to Khovanov homology ranks are hard problems.
Recently, it has been shown that Absolute Parallelism (AP) geometry admits paths that are naturally quantized. These paths have been used to describe the motion of spinning particles in a background gravitational field. In case of a weak static gravitational field limits, the paths are applied successfully to interpret…
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.
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.
BODE enhances deep neural network predictions and uncertainty quantification in safety modeling.
problem Uncertainty in deep neural network predictions for safety-critical applications.
method Bayesian optimization combined with deep ensembles (BODE).
result BODE reduces total uncertainty by over 30% compared to a manually tuned baseline ensemble.
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.
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.
Recent results and interpretations are presented for the thermal minority game, concentrating on deriving and justifying the fundamental stochastic differential equation for the microdynamics.
Study the Hessian geometry of an ideal gas in a centrifuge.
problem Understanding the Hessian geometry of an ideal gas in a centrifuge.
method Investigate the Hessian geometry associated with an ideal gas in a spherical centrifuge, using the action of the Euclidean rotation group.
result The Hessian geometry of a spherical rigid body is isometric to a hyperbolic space in the high angular velocity limit.
Quantum annealers aim at solving non-convex optimization problems by exploiting cooperative tunneling effects to escape local minima. The underlying idea consists in designing a classical energy function whose ground states are the sought optimal solutions of the original optimization problem and add a controllable qua…
We consider the problem of optimal trading for a power producer in the context of intraday electricity markets. The aim is to minimize the imbalance cost induced by the random residual demand in electricity, i.e. the consumption from the clients minus the production from renewable energy. For a simple linear price impa…
Dual ML approach predicts peak temperatures in AFSD, improving process optimization.
problem Lack of understanding between process parameters and resulting microstructure in AFSD.
method Combines supervised machine learning and physics-informed neural networks.
result Ensemble techniques like gradient boosting outperform other SML methods in predicting peak temperatures.
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.