Thermodynamics of learning physical phenomena

Chinesta, Francisco

doi:10.48550/arxiv.2207.12749

Cited by 2 publications

(3 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers in the domains of chemical and biological engineering are interested in solvation-free energy because it is an extra property that is vital to thermodynamics. The collection and maintenance of solvation properties have been handled by several databases, including ESOL (Estimated Solubility) [17], FreeSolv (The Free Solvation Database) [18], and MNsol (The Minnesota Solvation Database) [19,20]. Lin et al [7] developed a DL model for solvation-free energy in generic organic solvents and termed it Delfos [20].…”

Section: Solvation-free Energymentioning

confidence: 99%

“…The collection and maintenance of solvation properties have been handled by several databases, including ESOL (Estimated Solubility) [17], FreeSolv (The Free Solvation Database) [18], and MNsol (The Minnesota Solvation Database) [19,20]. Lin et al [7] developed a DL model for solvation-free energy in generic organic solvents and termed it Delfos [20]. MNsol served as the basis for this model.…”

Section: Solvation-free Energymentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Molecular Thermodynamics

2022

View full text Add to dashboard Cite

The methods used in chemical engineering are strongly reliant on having a solid grasp of the thermodynamic features of complex systems. It is difficult to define the behavior of ions and molecules in complex systems and to make reliable predictions about the thermodynamic features of complex systems across a wide range. Deep learning (DL), which can provide explanations for intricate interactions that are beyond the scope of traditional mathematical functions, would appear to be an effective solution to this problem. In this brief Perspective, we provide an overview of DL and review several of its possible applications within the realm of chemical engineering. DL approaches to anticipate the molecular thermodynamic characteristics of a broad range of systems based on the data that are already available are also described, with numerous cases serving as illustrations.

show abstract

Section: Solvation-free Energymentioning

confidence: 99%

Section: Solvation-free Energymentioning

confidence: 99%

Deep Learning for Molecular Thermodynamics

2022

View full text Add to dashboard Cite

show abstract

“…This motivates the development of computationally feasible approaches in this realm. A recently emerging third alternative in literature to this end is employing data-driven surrogate models devising machine learning, see, e.g., [12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33] and the references therein. Deploying the training costs offline materializing simulation or experimental data, these models surpass conventional rule-based approaches by drastically reducing the computational cost required during the prediction phase [34,35,36].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Aided Multiscale Magnetostatics

Aldakheel¹,

Soyarslan²,

Palanisamy³

et al. 2023

Preprint

View full text Add to dashboard Cite

Computational material modeling using advanced numerical techniques speeds up the design process and reduces the costs of developing new engineering products. In the field of multiscale modeling, huge computation efforts are expected for modeling heterogeneous materials while trying to reach high accuracy levels. In this work, a machine learning approach, namely the convolutional neural network (CNN), is developed as a solution providing a high level of accuracy, while being computationally efficient. The input for the CNN model consists of two-/three-dimensional images of artificial periodic and biphasic microstructures in the form of nonoverlapping and overlapping, mono-and polydisperse circular/spherical disk systems, which are generated by a random sequential inhibition process. These correspond to Statistical Volume Elements (SVE). Considering linear magnetostatics at the microscale, the output is the apparent permeability of the SVE. Training and testing data for the apparent properties is produced with finite element method-based two-scale asymptotic homogenization. The model efficiency is revealed by employing some representative examples in two-and three-dimensional settings. In this regard, the performance of the CNN model is assessed with the applied computational homogenization method relating to the accuracy and computational efficiency. The results with the CNN model show high accuracy in predicting the homogenized permeability and a significant decrease in computation time.

show abstract

Thermodynamics of learning physical phenomena

Cited by 2 publications

References 76 publications

Deep Learning for Molecular Thermodynamics

Deep Learning for Molecular Thermodynamics

Machine Learning Aided Multiscale Magnetostatics

Contact Info

Product

Resources

About