Study improves accuracy of weather data for real-time building simulations.
problem Anomalous and missing weather data affect real-time building energy simulations.
method Introduces a framework for quality control of measured weather data using anomaly detection and neural network infilling.
result Neural Networks enhance the accuracy of data imputation compared to traditional methods.
Automates building structural design with reduced mass and carbon footprint.
problem Time-consuming and laborious manual design process for buildings.
method Formulated building structures as graphs, trained end-to-end pipeline with a differentiable simulator.
result Optimal structural designs comparable to GA, with reduced building mass and carbon footprint.
Deep learning model predicts window openings for commercial buildings.
problem Window openings are often biased and require tuning for each occupant.
method Deep learning model trained on occupant data from multiple buildings.
result Model achieves high evaluation accuracy and F1 scores.
Bayesian deep learning improves building energy simulation accuracy.
problem Uncertainty in surrogate models for building energy performance.
method Training dropout neural networks and stochastic variational Gaussian Processes.
result Surrogate models reduce errors by up to 30% with uncertainty-aware sampling.
New method efficiently simulates fluid flows across various conditions.
problem High computational cost in simulating fluid flows.
method Parameter-conditioned sequential generative modeling of neural networks.
result Trained models simulate fluid flows at orders of magnitude faster than traditional methods.
DiffTaichi enables fast, differentiable physical simulations with shorter code.
problem Building efficient differentiable physical simulators.
method Differentiable programming language (DiffTaichi) that generates gradients using source code transformations and a light-weight tape.
result Differentiable physical simulators written in DiffTaichi are faster and more concise than existing methods.
Classical methods to control heating systems are often marred by suboptimal performance, inability to adapt to dynamic conditions and unreasonable assumptions e.g. existence of building models. This paper presents a novel deep reinforcement learning algorithm which can control space heating in buildings in a computatio…
Fast emulators built with neural search accelerate expensive scientific simulations.
problem Slow execution of accurate simulations limits scientific discovery.
method Neural architecture search to build accurate emulators with limited data.
result Simulations accelerated by up to 2 billion times in various scientific fields.
A model predicts building damage locations in near real-time using intensity-based features.
problem Accurate and timely damage diagnosis of building structures after extreme events.
method Support vector machines and Bayesian optimization for probabilistic hazard intensity determination.
result The model achieves 83.1% accuracy in identifying damage locations in a reinforced concrete moment frame.
RFpredInterval package builds prediction intervals for random forests and boosted forests.
problem Quantifying uncertainty in random forest and boosted forest point predictions.
method 16 methods to build prediction intervals with random forests and boosted forests.
result The proposed method outperforms existing methods in building prediction intervals.
Motivation: In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of in…
Maximal Rate of Stepwise Uncertainty Reduction selects simulations to reduce uncertainty efficiently.
problem Efficiently estimating quantities of interest from multi-fidelity simulations.
method Bayesian sequential strategy that maximizes the ratio of expected uncertainty reduction to simulation cost.
result MR-SUR strategy unifies and provides principled approaches to develop new methods.
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.
New method speeds up Bayesian inference for complex simulators.
problem Challenges in Bayesian inference for complex stochastic simulators with intractable likelihood functions.
method Optimization Monte Carlo framework reformulated as deterministic optimization problems with gradient-based methods.
result Accurate posterior inference with reduced runtimes compared to existing methods.
This work improves generative models by using feedback from multiple dependent models.
problem Improving the performance of generative models in multi-agent systems.
method Building a hierarchical set-up of multiple dependent generative models and using feedback to improve lower-level models.
result The technique improves the performance of lower-level generative models under certain conditions.
The paper develops a faster surrogate model for simulators using hybrid methods.
problem The need for faster validation of automotive technologies using simulators.
method Testing classical methods and building hybrid models combining them.
result A hybrid surrogate model outperforms classical methods in multivariate time series prediction.
The paper analyzes portfolio management in the Heston model, proposing new strategies.
problem Investment performance influenced by asset diversity and cash inclusion.
method Monte Carlo simulations in the Heston model, MACD and RSI technical analysis.
result New portfolio management strategies based on MACD and RSI.
We introduce the simulation tool SABCEMM (Simulator for Agent-Based Computational Economic Market Models) for agent-based computational economic market (ABCEM) models. Our simulation tool is implemented in C++ and we can easily run ABCEM models with several million agents. The object-oriented software design enables th…
The paper quantifies and attributes uncertainty in complex system simulations.
problem Uncertainty in complex system simulations due to unknown or approximated subprocesses.
method Developed a framework for quantifying and attributing submodel uncertainty using bootstrapping, Bayesian model averaging, and tree-based methods.
result Individual submodels contribute to overall uncertainty, and their importance can be quantified.
MarS simulates financial markets using generative models.
problem Simulating realistic financial market effects.
method Order-level generative foundation model (LMM) for realistic, interactive, and controllable order generation.
result Strong scalability and robust realism in MarS.
A new method uses active learning to improve chemical simulation efficiency.
problem Efficiently estimating equilibrium-based chemical simulations.
method Sequential data-driven approach using Gaussian process uncertainty.
result Significantly reduced number of function evaluations.
Proposes method for eliciting non-parametric joint priors using normalizing flows.
problem Learning complex non-parametric joint priors for model parameters.
method Expert elicitation combined with normalizing flows for generative modeling.
result Framework supports elicitation of both parametric and non-parametric priors.
Efficient surrogate model reduces ESM evaluation time.
problem Reducing computational time for large-scale ESM simulations.
method SVD for dimensionality reduction followed by Bayesian optimization for neural network surrogate.
result 20 ESM simulations build a 42660-variable surrogate with 93% consistency and 2% MSE.
Enhances kernel regression with network data for better predictions.
problem Improving predictive power in high-dimensional data.
method Combines kernel regression with network cohesion data to model nonlinearities.
result Significantly better predictive performances in high-dimensional data.
Versatile model for High Energy Physics events.
problem Modeling complex interactions in high-energy physics data.
method Energy-based probabilistic model with multi-purpose architecture.
result Achieves success in diverse applications like simulation, anomaly detection, and particle identification.
Study Figgie card game strategies using agent-based simulation.
problem Analyze strategies for Figgie card game and market behavior.
method Develop agent-based discrete-event market simulation to test strategies.
result Fundamentalist strategy is profit-maximizing in all tested combinations.
Enhances anomaly detection using multiple reference datasets.
problem Lack of performance with multiple reference datasets.
method Generalizes existing techniques CWoLa and SALAD for multiple reference datasets.
result Improved performance across various settings.
Indoor localization based on SIngle Of Fingerprint (SIOF) is rather susceptible to the changing environment, multipath, and non-line-of-sight (NLOS) propagation. Building SIOF is also a very time-consuming process. Recently, we first proposed a GrOup Of Fingerprints (GOOF) to improve the localization accuracy and reduc…
FinRL-Meta creates diverse market environments for DRL in finance.
problem Inaccurate financial data and diverse market environments challenge DRL in finance.
method Open-source data processing tools, hundreds of market environments, and multiprocessing.
result FinRL-Meta improves DRL accuracy and speed in financial simulations.
We propose a spectral clustering method based on local principal components analysis (PCA). After performing local PCA in selected neighborhoods, the algorithm builds a nearest neighbor graph weighted according to a discrepancy between the principal subspaces in the neighborhoods, and then applies spectral clustering. …
We consider the problem of performing matrix completion with side information on row-by-row and column-by-column similarities. We build upon recent proposals for matrix estimation with smoothness constraints with respect to row and column graphs. We present a novel iterative procedure for directly minimizing an informa…
Gamification and deep learning improve energy efficiency in smart buildings.
problem Lack of human engagement and motivation in smart building control.
method Modeling user interaction as a game, integrating IoT sensors and cyber-physical systems, using Deep Learning for forecasting.
result Improved energy efficiency through gamified smart building control.
Study examines market risks on pension system sustainability.
problem Impact of market risks on pension corpus sustainability.
method Monte Carlo simulations with historical data.
result Market risks significantly impact pension corpus sustainability.
Quantum computing techniques applied to Monte Carlo simulations in finance.
problem Efficiently simulating quantum algorithms for financial modeling.
method Introduces quantum computing basics, amplitude estimation, and Grover's algorithm for unstructured search.
result Demonstrates quantum approaches to Monte Carlo integration and counting in finance.
Two new regularization methods improve neural network performance and complexity control.
problem Improving neural network performance and complexity control with correlated or high-dimensional features.
method Two regularization strategies: covariance-aware ridge and covariance-aware lasso.
result Improves predictive performance and complexity control over standard penalties.
Paper proposes using generalized lambda distributions for stochastic simulators.
problem Uncertainty quantification with complex stochastic models is computationally challenging.
method Flexible generalized lambda distribution approximates response PDF, parameters are sparse polynomial chaos expansions.
result Local inference of response PDF at each point of experimental design using replicated model evaluations.
Complex network analysis reveals dominant stocks in financial stock returns correlations.
problem Inferring financial stock returns correlations from complex network analysis.
method Simulated geometric Brownian motion for stocks, complex network analysis, eigenvector centrality, clustering.
result Returns correlation matrix is dominated by stocks with high eigenvector centrality and clustering.
A new method combines simple binary classifiers to build complex multiclass classifiers, achieving performance limits in a Gaussian setting.
problem Building a sophisticated multiclass classifier from simple binary decisions.
method Combining O(logK) simple binary classifiers to form a K-class classifier. result Explicit performance bounds across various decoding and dimensional regimes for a stylized Gaussian setting.
Study optimizes building energy control and power planning using RL.
problem Optimizing academic buildings' HVAC and power systems.
method Reinforcement Learning (RL) for scheduling and planning.
result Algorithm optimizes hourly energy usage and handles short-term changes.
Bayesian optimisation tackles expensive black-box functions with constraints.
problem Optimizing constrained black-box functions in machine learning and simulation.
method Proposes a new Knowledge Gradient acquisition function for constrained Bayesian optimisation.
result Demonstrates superior performance over four state-of-the-art constrained Bayesian optimisation algorithms.
Bayesian segmentation and uncertainty estimation improve 3D model accuracy for factory planning.
problem Generating accurate 3D models from outdated and incomplete 2D data.
method Bayesian neural network for point cloud segmentation and entropy-based uncertainty estimation.
result Bayesian segmentation network significantly improves model accuracy and object identification.
MF-GLaM models improve stochastic simulator emulation with multifidelity data.
problem Challenging to emulate stochastic simulators' full conditional probability distribution.
method Proposes MF-GLaMs to efficiently emulate HF stochastic simulators using LF data.
result MF-GLaMs achieve improved accuracy or comparable performance at reduced cost.
A transfer learning method builds high-dimensional models using disparate datasets.
problem Building comprehensive prediction models with small sample sizes and limited features.
method Transfer learning approach using external data to build a reduced model and apply calibration equations.
result Proposes a penalized generalized method of moment framework for inference and one-step estimation.
The study improves GCN performance on friendship-based social networks by simulating and augmenting datasets.
problem GCNs' limitations in friendship-based social networks, especially dependency between node neighbourhood order and layer.
method Developed a Python library to simulate social networks with ground truth labels and features. Introduced sDNA as latent variables for nodes.
result Four new GCN variants significantly outperform the original model on 27 out of 30 simulated datasets.
New method boosts BOED using SBI and neural likelihood.
problem Maximizing EIG in BOED with intractable likelihood.
method Neural likelihood estimation, multi-start gradient ascent.
result Significantly improved BOED performance over state-of-the-art.
Algorithm uncovers treatment effect heterogeneity in educational RD designs.
problem Discovering sources of treatment effect heterogeneity in regression discontinuity designs.
method Causal supervised machine learning algorithm to build a 'regression discontinuity tree'.
result Algorithm uncovers various sources of heterogeneity in the impact of attending a better secondary school.
Learning models of artificial intelligence can nowadays perform very well on a large variety of tasks. However, in practice different task environments are best handled by different learning models, rather than a single, universal, approach. Most non-trivial models thus require the adjustment of several to many learnin…
In risk management it is desirable to grasp the essential statistical features of a time series representing a risk factor. This tutorial aims to introduce a number of different stochastic processes that can help in grasping the essential features of risk factors describing different asset classes or behaviors. This pa…