PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders

Recent advances in generative AI offer promising solutions for synthetic data generation but often rely on large datasets for effective training. To address this limitation, we propose a novel generative model that learns from limited data by incorporating physical constraints to enhance performance. Specifically, we extend the VAE architecture by incorporating physical models in the generative process, enabling it to capture underlying dynamics more effectively. While physical models provide valuable insights, they struggle to capture complex temporal dependencies present in real-world data. To bridge this gap, we introduce a discrepancy term to account for unmodeled dynamics, represented within a latent Gaussian Process VAE (GPVAE). Furthermore, we apply regularization to ensure the generated data aligns closely with observed data, enhancing both the diversity and accuracy of the synthetic samples. The proposed method is applied to indoor temperature data, achieving state-of-the-art performance. Additionally, we demonstrate that PIGPVAE can produce realistic samples beyond the observed distribution, highlighting its robustness and usefulness under distribution shifts.

Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Environments

Forecasting indoor temperatures is important to achieve efficient control of HVAC systems. In this task, the limited data availability presents a challenge as most of the available data is acquired during standard operation where extreme scenarios and transitory regimes such as major temperature increases or decreases are de-facto excluded. Acquisition of such data requires significant energy consumption and a dedicated facility, hindering the quantity and diversity of available data. Cost related constraints however do not allow for continuous year-around acquisition. To address this, we investigate the efficacy of data augmentation techniques leveraging SoTA AI-based methods for synthetic data generation. Inspired by practical and experimental motivations, we explore fusion strategies of real and synthetic data to improve forecasting models. This approach alleviates the need for continuously acquiring extensive time series data, especially in contexts involving repetitive heating and cooling cycles in buildings. In our evaluation 1) we assess the performance of synthetic data generators independently, particularly focusing on SoTA AI-based methods; 2) we measure the utility of incorporating synthetically augmented data in a subsequent forecasting tasks where we employ a simple model in two distinct scenarios: 1) we first examine an augmentation technique that combines real and synthetically generated data to expand the training dataset, 2) we delve into utilizing synthetic data to tackle dataset imbalances. Our results highlight the potential of synthetic data augmentation in enhancing forecasting accuracy while mitigating training variance. Through empirical experiments, we show significant improvements achievable by integrating synthetic data, thereby paving the way for more robust forecasting models in low-data regime.

Learning Physics between Digital Twins with Low-Fidelity Models and Physics-Informed Gaussian Processes

A digital twin is a computer model that represents an individual, for example, a component, a patient or a process. In many situations, we want to gain knowledge about an individual from its data while incorporating imperfect physical knowledge and also learn from data from other individuals. In this paper, we introduce and demonstrate a fully Bayesian methodology for learning between digital twins in a setting where the physical parameters of each individual are of interest. For each individual, the methodology is based on Bayesian calibration with model discrepancy. Through the discrepancy, modelled as a Gaussian process, the imperfect low-fidelity physical model is accounted for. Using ideas from Bayesian hierarchical models, a joint probabilistic model of digital twins is constructed by connecting them through a new level in the hierarchy. For the physical parameters, the methodology can be seen as using a prior distribution in the individual model that is the posterior of the corresponding hyperparameter in the joint model. For learning the imperfect physics between individuals two approaches are introduced, one that assumes the same discrepancy for all individuals and one that can be seen as using a prior learned from all individuals for the parameters of the Gaussian processes representing the discrepancies. Based on recent advances related to physics-informed priors, Hamiltonian Monte Carlo methods and using these for inverse problems we set up an inference methodology that allows our approach to be computational feasible also for physical models based on partial differential equations and individual data that are not aligned. The methodology is demonstrated in two synthetic case studies, a toy example previously used in the literature extended to more individuals and an example based on a cardiovascular differential equation model relevant for the treatment of hypertension.

Bayesian Calibration of imperfect computer models using Physics-informed priors

In this work we introduce a computational efficient data-driven framework suitable for quantifying the uncertainty in physical parameters of computer models, represented by differential equations. We construct physics-informed priors for differential equations, which are multi-output Gaussian process (GP) priors that encode the model's structure in the covariance function. We extend this into a fully Bayesian framework which allows quantifying the uncertainty of physical parameters and model predictions. Since physical models are usually imperfect descriptions of the real process, we allow the model to deviate from the observed data by considering a discrepancy function. For inference Hamiltonian Monte Carlo (HMC) sampling is used. This work is motivated by the need for interpretable parameters for the hemodynamics of the heart for personal treatment of hypertension. The model used is the arterial Windkessel model, which represents the hemodynamics of the heart through differential equations with physically interpretable parameters of medical interest. As most physical models, the Windkessel model is an imperfect description of the real process. To demonstrate our approach we simulate noisy data from a more complex physical model with known mathematical connections to our modeling choice. We show that without accounting for discrepancy, the posterior of the physical parameters deviates from the true value while when accounting for discrepancy gives reasonable quantification of physical parameters uncertainty and reduces the uncertainty in subsequent model predictions.