A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications

Zobeiry, Navid; Humfeld, Keith D.

doi:10.1016/j.engappai.2021.104232

Cited by 210 publications

(75 citation statements)

References 23 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Crucially, this will ensure that forecasting remains significantly computationally cheaper than the usage of wave models. These methods have been successfully applied to the solving of differential equations in engineering (Niaki et al, 2021;Zobeiry, and Humfeld, 2021), analyzing blood flow (Arzani et al, 2021), and chaotic systems (Khodkar and Hassanzadeh, 2021). Relevant for the current discussion, these methods are also finding use in weather and climate modelling (Kashinath et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Forecasting Hurricane-forced Significant Wave Heights using the Long Short-Term Memory Network in the Caribbean Sea

Bethel

Sun

2021

Preprint

View full text Add to dashboard Cite

Abstract. A Long Short-Term Memory (LSTM) neural network is proposed to predict hurricane-forced significant wave heights (SWH) in the Caribbean Sea (CS) based on a dataset of 20 CS, Gulf of Mexico, and Western Atlantic hurricane events collected from 10 buoys from 2010–2020. SWH nowcasting and forecasting are initiated using LSTM on 0-, 3-, 6-, 9-, and 12-hour horizons. Through examining study cases Hurricanes Dorian (2019), Sandy (2012), and Igor (2010), results illustrate that the model is well suited to forecast hurricane-forced wave heights. Forecasts are highly accurate with regard to observations. For example, Hurricane Dorian nowcasts had correlation (R), root mean square error (RMSE), and mean absolute percentage error (MAPE) values of 0.99, 0.16 m, and 2.6 %, respectively. Similarly, on the 3-, 6-, 9-, and 12-hour forecasts, results produced R (RMSE; MAPE) values of 0.95 (0.51 m; 7.99 %), 0.92 (0.74 m; 10.83 %), 0.85 (1 m; 13.13 %), and 0.84 (1.24 m; 14.82 %), respectively. However, the model also consistently over-predicted the maximum observed SWHs. To improve models results, additional research should be geared towards improving single-point LSTM neural network training datasets by considering hurricane track and identifying the hurricane quadrant in which buoy observations are made.

show abstract

Section: Discussionmentioning

confidence: 99%

Forecasting Hurricane-forced Significant Wave Heights using the Long Short-Term Memory Network in the Caribbean Sea

Bethel

Sun

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In this context, many gaps and opportunities remain for further research in building ML-based models for use in composite manufacturing environments, which meet the human-interpretable needs for tractable risk reduction. Recent advances in pre-processing datasets, such as physics-informed ML [40], has provided increased interpretability for model designers by directly influencing the learning process in a common physics-based framework, towards using models for interpolation-based predictions. Further, with the recent emergence of AI explainability tools such as Local Interpretable Model-Agnostic Explanations (LIME)-based toolboxes, or the Shapley val-Open Journal of Composite Materials ue-based approach used by Fiddler Labs [43], there is an opportunity for further research in this field to address the above concerns, in both the construction and evaluation of models, and associated datasets for advanced manufacturing.…”

Section: Future Perspectivesmentioning

confidence: 99%

A Mini-Review and Perspective on Current Best Practice and Emerging Industry 4.0 Methods for Risk Reduction in Advanced Composites Manufacturing

Crawford¹,

Khayyam²,

Milani³

2021

OJCM

View full text Add to dashboard Cite

The manufacturing of composite structures is a highly complex task with inevitable risks, particularly associated with aleatoric and epistemic uncertainty of both the materials and processes, as well as the need for in-situ decision-making to mitigate defects during manufacturing. In the context of aerospace composites production in particular, there is a heightened impetus to address and reduce this risk. Current qualification and substantiation frameworks within the aerospace industry define tractable methods for risk reduction. In parallel, Industry 4.0 is an emerging set of technologies and tools that can enable better decision-making towards risk reduction, supported by data-driven models. It offers new paradigms for manufacturers, by virtue of enabling in-situ decisions for optimizing the process as a dynamic system.However, the static nature of current (pre-Industry 4.0) best-practice frameworks may be viewed as at odds with this emerging novel approach. In addition, many of the predictive tools leveraged in an Industry 4.0 system are black-box in nature, which presents other concerns of tractability, interpretability and ultimately risk. This article presents a perspective on the current state-of-the-art in the aerospace composites industry focusing on risk reduction in the autoclave processing, as an example system, while reviewing current trends and needs towards a Composites 4.0 future.

show abstract

“…PINN has resulted in excitement on the use of machine learning algorithms for solving physical systems and optimizing their characteristic parameters given data. PINNs have now been applied to solve a variety of problems including fluid mechanics [54,33,20,72,10,55,46,57], solid mechanics [24,56,23,21], heat transfer [11,48,76], electro-chemistry [53,43,32], electro-magnetics [16,13,49], geophysics [7,63,62,66], and flow in porous media [19,1,6,60,34,5,64] (for a detailed review, see [35]). A few libraries have also been developed for solving PDEs using PINNs, including SciANN [22], DeepXDE [44], SimNet [25], and NeuralPDE [77].…”

Section: Introductionmentioning

confidence: 99%

Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training

Haghighat,

Amini,

Juanes

2021

Preprint

View full text Add to dashboard Cite

Physics-informed neural networks (PINNs) have received significant attention as a unified framework for forward, inverse, and surrogate modeling of problems governed by partial differential equations (PDEs). Training PINNs for forward problems, however, pose significant challenges, mainly because of the complex non-convex and multi-objective loss function. In this work, we present a PINN approach to solving the equations of coupled flow and deformation in porous media for both single-phase and multiphase flow. To this end, we construct the solution space using multi-layer neural networks. Due to the dynamics of the problem, we find that incorporating multiple differential relations into the loss function results in an unstable optimization problem, meaning that sometimes it converges to the trivial null solution, other times it moves very far from the expected solution. We report a dimensionless form of the coupled governing equations that we find most favourable to the optimizer. Additionally, we propose a sequential training approach based on the stress-split algorithms of poromechanics. Notably, we find that sequential training based on stress-split performs well for different problems, while the classical strain-split algorithm shows an unstable behaviour similar to what is reported in the context of finite element solvers. We use the approach to solve benchmark problems of poroelasticity, including Mandel's consolidation problem, Barry-Mercer's injection-production problem, and a reference two-phase drainage problem. The Python-SciANN codes reproducing the results reported in this manuscript will be made publicly available at https://github.com/sciann/sciann-applications.

show abstract

A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications

Cited by 210 publications

References 23 publications

Forecasting Hurricane-forced Significant Wave Heights using the Long Short-Term Memory Network in the Caribbean Sea

Forecasting Hurricane-forced Significant Wave Heights using the Long Short-Term Memory Network in the Caribbean Sea

A Mini-Review and Perspective on Current Best Practice and Emerging Industry 4.0 Methods for Risk Reduction in Advanced Composites Manufacturing

Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training

Contact Info

Product

Resources

About