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.
CityTFT models urban building energy using a data-driven approach.
Advances in renewable energy generation and introduction of the government targets to improve energy efficiency gave rise to a concept of a Zero Energy Building (ZEB). A ZEB is a building whose net energy usage over a year is zero, i.e., its energy use is not larger than its overall renewables generation. A collection …
The area of building energy management has received a significant amount of interest in recent years. This area is concerned with combining advancements in sensor technologies, communications and advanced control algorithms to optimize energy utilization. Reinforcement learning is one of the most prominent machine lear…
Versatile model for High Energy Physics events.
Study shows visual feedback and monetary incentives reduce plugload energy consumption in commercial buildings.
New deep learning model optimizes energy use in buildings.
In this paper, we propose a gamification approach as a novel framework for smart building infrastructure with the goal of motivating human occupants to reconsider personal energy usage and to have positive effects on their environment. Human interaction in the context of cyber-physical systems is a core component and c…
Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the smart energy management at the building level is given by the problem of real-time detection of flexible demand available. In this paper we pr…
Deep Autoencoder outperforms in anomaly detection for building energy data.
Thermal dynamics modeling has been a critical issue in building heating, ventilation, and air-conditioning (HVAC) systems, which can significantly affect the control and maintenance strategies. Due to the uniqueness of each specific building, traditional thermal dynamics modeling approaches heavily depending on physics…
Bayesian deep learning improves building energy simulation accuracy.
New estimates for Hitchin's equations at high energy.
The implementation of smart building technology in the form of smart infrastructure applications has great potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. However, human preference in regard to living conditions is usually unknown and heterogeneous in its manifestati…
This paper focuses on energy management in buildings with phase change material (PCM), which is primarily used to improve thermal performance, but can also serve as an energy storage system. In this setting, optimal scheduling of an HVAC system is challenging because of the nonlinear and non-convex characteristics of t…
This paper presents a novel deep learning architecture for short term load forecasting of building energy loads. The architecture is based on a simple base learner and multiple boosting systems that are modelled as a single deep neural network. The architecture transforms the original multivariate time series into mult…
Study improves accuracy of weather data for real-time building simulations.
New proof shows spacetime energy is always positive in higher dimensions.
Physics-based framework improves building energy forecasting.
Rectifies singular set of harmonic maps into complex.
We address the problem of constructing numerical integrators for nonholonomic Lagrangian systems that enjoy appropriate discrete versions of the geometric properties of the continuous flow, including the preservation of energy. Building on previous work on time-dependent discrete mechanics, our approach is based on a d…
Paper proposes PI-DAE for missing data imputation in buildings using physics constraints.
Paper proposes new loss functions for training energy networks.
Model predicts climate change's impact on real estate prices.
This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamic filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependen…
In this paper we build an explicit example of a minimal bubble on a Willmore surface, showing there cannot be compactness for Willmore immersions of Willmore energy above . Additionnally we prove an inequality on the second residue for limits sequences of Willmore immersions with simple minimal bubbles. Doing so,…
Computer simulations are invaluable tools for scientific discovery. However, accurate simulations are often slow to execute, which limits their applicability to extensive parameter exploration, large-scale data analysis, and uncertainty quantification. A promising route to accelerate simulations by building fast emulat…
We prove that a random group of the graph model associated with a sequence of expanders has fixed-point property for a certain class of CAT(0) spaces. We use Gromov's criterion for fixed-point property in terms of the growth of n-step energy of equivariant maps from a finitely generated group into a CAT(0) space, to wh…
Unified geometric description of Kepler flow across all energies.
Paper introduces a modified Allen-Cahn equation for better energy equipartition.
Proves strict inequality for minimizers of Willmore energy under isoperimetric constraints.
Increasing energy efficiency in buildings can reduce costs and emissions substantially. Historically, this has been treated as a local, or single-agent, optimization problem. However, many buildings utilize the same types of thermal equipment e.g. electric heaters and hot water vessels. During operation, occupants in t…
New model predicts energy prices volatility by smoothing time variation and persistence.
The paper improves energy contract pricing models by incorporating jumps and varying parameters.
Researchers find a surface with minimum bending energy for any genus and isoperimetric ratio.
We use matricial free energy to regularize autoencoders, producing Gaussian-like codes.
Energy consumption for hot water production is a major draw in high efficiency buildings. Optimizing this has typically been approached from a thermodynamics perspective, decoupled from occupant influence. Furthermore, optimization usually presupposes existence of a detailed dynamics model for the hot water system. The…
EB-GFN models discrete data with amortized MCMC sampling.
A new AI optimization method uses energy-conserving dynamics inspired by Born-Infeld theory.
A brisk building boom of hydropower mega-dams is underway from China to Brazil. Whether benefits of new dams will outweigh costs remains unresolved despite contentious debates. We investigate this question with the "outside view" or "reference class forecasting" based on literature on decision-making under uncertainty …
Machine learning predicts molecular crystal stability.
The paper extends rigidity results to non-compact domains and infinite energy maps.
This paper generalizes neural transport learning for free energy estimation in arbitrary state spaces.
A generalized gamification framework is introduced as a form of smart infrastructure with potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. The proposed framework enables a Human-Centric Cyber-Physical System using an interface to allow building managers to interact wi…
Classifies pinned -elasticae and finds unique optimality exponents.
Develops a new bivariate process for energy markets with improved simulation methods.
New method uses neural networks to improve free energy estimation.
Improved generative models using overparametrized shallow neural networks.
Faults in HVAC systems degrade thermal comfort and energy efficiency in buildings and have received significant attention from the research community, with data driven methods gaining in popularity. Yet the lack of labeled data, such as normal versus faulty operational status, has slowed the application of machine lear…