A physics-aware learning architecture with input transfer networks for predictive modeling

Behjat, Amir; Rai, Rahul; Matei, Ion; Doermann, David; Chowdhury, Souma

doi:10.1016/j.asoc.2020.106665

Cited by 9 publications

(8 citation statements)

References 25 publications

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“…To address these issues, a novel hybrid architecture was introduced by Behjat et al [25] which uses partial physics as an additional node in the model. This model was called "Opportunistic Physics-mining Transfer Mapping Architecture" or OPTMA, and it was used in a dynamics prediction problem, for UAV flight prediction under gust [26]; and in a CFD simulation modeling problem, to explore bio-inspired surface riblets for drag Reduction [27]. While OPTMA outperformed data-driven and classical hybrid architectures, it was challenging to train it in a gradient-based way without generating the labels.…”

Section: A Physics-infused Hybrid Modelingmentioning

confidence: 99%

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Efficient Training of Transfer Mapping in Physics-Infused Machine Learning Models of UAV Acoustic Field

Iqbal¹,

Behjat²,

Adlakha³

et al. 2022

Preprint

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Physics-Infused Machine Learning (PIML) architectures aim at integrating machine learning with computationallyefficient, low-fidelity (partial) physics models, leading to improved generalizability, extrapolability, and robustness to noise, compared to pure data-driven approximation models. End-uses of PIML include, but are not limited to, model-based optimization and model-predictive control. Recently a new PIML architecture was reported by the same authors, known as Opportunistic Physics-mining Transfer Mapping Architecture or OPTMA, which transfers the original inputs into latent features using a transfer neural network; the partial physics model then uses the latent features to generate the final output that is as close as possible to the high-fidelity output. While gradient-free solvers and back-propagation with supervised learning (where optimum labels are pre-generated) have been used to train OPTMA, that approach remains computationally inefficient, and challenging to generalize across different problems or exploit state-of-theart machine learning architectures. This paper aims to alleviate these issues by infusing the partial physics model inside the neural network, as described via tensors in the popular machine learning framework, PyTorch. Such a description also naturally allows auto-differentiation of the partial physics model, thereby enabling the use of efficient back-propagation methods to train the transfer network. The benefits of the upgraded OPTMA architecture with Automatic Differentiation (OPTMA-Net) is demonstrated by applying it to the problem of modeling the sound pressure field created by a hovering unmanned aerial vehicle (UAV). Ground truth data for this problem has been obtained from an indoor UAV noise measurement setup. Here, the partial physics model is based on the interference of acoustic pressure waves generated by an arbitrary number of acoustic monopole sources. Case studies show that OPTMA-Net provides generalization performance close to, and extrapolation performance that is 4 times better than, those given by a pure data-driven model.

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Section: A Physics-infused Hybrid Modelingmentioning

confidence: 99%

“…Previously, Particle Swarm Optimization has been used to train OPTMA [26]. This is however, computationally very expensive and would not be viable for complex physics-based models.…”

Section: A Previous Frameworkmentioning

confidence: 99%

Efficient Training of Transfer Mapping in Physics-Infused Machine Learning Models of UAV Acoustic Field

Iqbal¹,

Behjat²,

Adlakha³

et al. 2022

Preprint

Self Cite

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“…where L is the lift, D is the drag, W is the aircraft weight, and Th is the thrust. Equation (12) denotes that thrust required is the drag, and equals to the following aerodynamic equation:…”

Section: Cruise Fuel Consumption Dynamicsmentioning

confidence: 99%

“…The Mach number distribution is skewed, and most of the population is around 0.83, which is usually the nominal cruise speed regime for this aircraft. For a greater gross weight, an autopilot system sets throttle to a higher position to maintain Equation (12). Figure 6 depicts gross weight correlation with the throttle δ T , and fuel consumption from data.…”

Section: Cruise Fuel Consumption Dynamicsmentioning

confidence: 99%

“…In particular, even though state-of-the-art ML models can seize entangled spatial-temporal correlations and relations, they require a vast amount of labeled data for training and testing, seldom available in real applications. [12] In addition to this, ML algorithms and methods often give scientifically discrepant results. They can only effectively capture relations in the possible training data and hence have a poor outof-sample generalization capability [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Physics Guided Deep Learning for Data-Driven Aircraft Fuel Consumption Modeling

Uzun

Demirezen²,

Tsourdos

2021

Aerospace

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This paper presents a physics-guided deep neural network framework to estimate fuel consumption of an aircraft. The framework aims to improve data-driven models’ consistency in flight regimes that are not covered by data. In particular, we guide the neural network with the equations that represent fuel flow dynamics. In addition to the empirical error, we embed this physical knowledge as several extra loss terms. Results show that our proposed model accomplishes correct predictions on the labeled test set, as well as assuring physical consistency in unseen flight regimes. The results indicate that our model, while being applicable to the aircraft’s complete flight envelope, yields lower fuel consumption error measures compared to the model-based approaches and other supervised learning techniques utilizing the same training data sets. In addition, our deep learning model produces fuel consumption trends similar to the BADA4 aircraft performance model, which is widely utilized in real-world operations, in unseen and untrained flight regimes. In contrast, the other supervised learning techniques fail to produce meaningful results. Overall, the proposed methodology enhances the explainability of data-driven models without deteriorating accuracy.

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Physics-informed machine learning in prognostics and health management: State of the art and challenges

Deng

Nguyen

Medjaher

et al. 2023

Applied Mathematical Modelling

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A physics-aware learning architecture with input transfer networks for predictive modeling

Cited by 9 publications

References 25 publications

Efficient Training of Transfer Mapping in Physics-Infused Machine Learning Models of UAV Acoustic Field

Efficient Training of Transfer Mapping in Physics-Infused Machine Learning Models of UAV Acoustic Field

Physics Guided Deep Learning for Data-Driven Aircraft Fuel Consumption Modeling

Physics-informed machine learning in prognostics and health management: State of the art and challenges

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