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The application of Artificial Neural Networks (ANNs) has been extensively investigated for fluid dynamic problems. A specific form of ANNs are Physics-Informed Neural Networks (PINNs). They incorporate physical laws in the training and have ...

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• PINNs improved the ANN prediction accuracy for the potential flow cases in this work.
• Random distribution of training data lead to a higher prediction accuracy of ANNs.
• A Sequence-to-sequence method enabled temporal interpolation ...

We present an efficient dimension-by-dimension finite-volume method which solves the adiabatic magnetohydrodynamics equations at high discretization order, using the constrained-transport approach on Cartesian grids. Results are presented up to ...

Quantum computing is an advancing area of research in which computer hardware and algorithms are developed to take advantage of quantum mechanical phenomena. In recent studies, quantum algorithms have shown promise in solving linear systems of ...

• Dynamic mode decomposition is recast as an optimization problem to propagate the state of the linearized dynamical system on a quantum computer.
• Results for this work are demonstrated for a quantum computer simulator.
• Quadratic ...

A physics-informed neural network was developed for estimating a solution to a multi-physics problem involving electromagnetism, fluid dynamics, and heat transfer. The multi-physical phenomenon was modeled on a cylindrical conductor with ...

Colonic motility plays a vital role in maintaining proper digestive function. The rhythmic contractions and relaxations facilitate various types of motor functions that generate both propulsive and non-propulsive motility modes which in turn ...

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• A novel CFD model simulating shear stress distribution in the colon with varying mucus thicknesses.
• Characterization of shear stress on colonic epithelium in ulcerative colitis conditions with severe mucosal damage.
• Significant ...

An emerging trend in deep learning research focuses on the applications of graph neural networks (GNNs) for mesh-based continuum mechanics simulations. Most of these learning frameworks operate on graphs wherein each edge connects two nodes. ...

• Data-driven surrogate model via hypergraph neural network.
• Finite element-inspired hypergraph connectivity and network architecture.
• Stabilized and accurate predictions of flow around bluff and streamlined bodies.
• Accurate lift ...

Physics-Informed Neural Networks (PINN) have emerged as a formidable tool for addressing sophisticated computational physics challenges, offering an innovative approach to integrating physical laws directly into deep learning models. By incorporating the ...

Ordinary differential equations (ODEs) play a crucial role in applied mathematics, engineering, and various other fields. With a rich history of study and countless applications, they have attracted the attention of researchers who have developed ...

We propose a finite-element formulation for simulating multi-component flows occupying the same domain with spatially varying concentrations. Each constituent is assumed to behave as an incompressible Newtonian fluid, and solutions are sought for ...

• First stabilized FEM for dispersed flows based on orthogonal subgrid scales.
• Term-by-term stabilization requires few additional terms in the weak form.
• Optimal error convergence despite approximate residual owing to orthogonal ...

Fluid dynamics problems are characterized by being multidimensional and nonlinear. Therefore, experiments and numerical simulations are complex and time-consuming. Motivated by this, the need arises to find new techniques to obtain data in a ...

• New model to reconstruct complex fluid databases from sparse measurements.
• New hybrid deep learning and physic aware models.
• Simple, robust and generalizable deep learning architectures.
• Reconstruct three-dimensional databases ...

This work presents a family of bounded schemes constructed based on the concept of Bézier curves that satisfies the TVD, CBC, and BAIR stability criteria for the numerical solution of convection-dominated transport problems in computational fluid ...

Deterministic Lateral Displacement (DLD) device has gained widespread recognition and trusted for filtering blood cells. However, there remains a crucial need to explore the complex interplay between deformable cells and flow within the DLD ...

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• A numerical model is developed and validated to simulate the motion and deformation of red blood cells (RBCs) in microfluidic devices.
• The model accurately reproduces characteristic trajectory patterns and critical row-shift fractions ...

This work aims to improve fuel chamber injectors’ performance in turbofan engines, thus implying improved performance and reduction of pollutants. This requires the development of models that allow real-time prediction and improvement of the fuel/...

• Forecasting deep learning models can be used to predict future flow dynamics.
• Modal decomposition techniques can improve neural networks training performance.
• Explore the limits of deep learning forecasting applied to complex flow ...

The correlation and extraction of coherent structures from a turbulent flow is a principal objective of data-driven modal decomposition techniques. The Conditional space-time Proper Orthogonal Decomposition (CST-POD) offers insight into transient ...

• Conditional space-time proper orthogonal decomposition extensions and best practices.
• New analyses methods to educe causality and stability of extreme flow events.
• The decomposition of resonance modes into forcing and response ...

The regularized optimal mass transport (rOMT) problem adds a diffusion term to the continuity equation in the original dynamic formulation of the optimal mass transport (OMT) problem proposed by Benamou and Brenier. We show that the rOMT model ...

• The electrical analog model is expanded to permeable decellularized organs.
• The ...

In recellularization, cell-seeding efficiency refers to the uniform distribution of cells across the decellularized organ, which should be enhanced to ensure effective functioning. During cell ...

This work addresses model order reduction for complex moving fronts, which are transported by advection or through a reaction–diffusion process. Such systems are especially challenging for model order reduction since the transport cannot be ...

In Colombeau (Z Angew Math Phys 66:2575–2599, 2015), Colombeau (Z Angew Math Phys 71:112, 2020) we constructed weak asymptotic solutions to some systems of fluid dynamics on the n-D torus ${\mathbb{T}}^n$. In Colombeau (Z Angew Math Phys 71:112, 2020), we ...${\mathbb{}}^{}$${}_{\mathrm{}}{}^{\mathrm{}}{\mathbb{}}^{}{\mathbf{}}_{\mathrm{}}{}^{}{{\mathbb{}}^{}}^{}$

We propose an efficient semi-Lagrangian Characteristic Mapping (CM) method for solving the three-dimensional (3D) incompressible Euler equations. This method evolves advected quantities by discretizing the flow map associated with the velocity ...

• A semi-Lagrangian approach with third-order global convergence.
• Accurate long-time conservation of energy and helicity (Table 4.1).
• Arbitrary subgrid resolution and non-dissipative evolution of the solution (Figs. 4.3, 4.9 and 4.10)...

• First successful discretization of the unified model of continuum mechanics with an SPH scheme.

Smoothed Particle Hydrodynamics (SPH) methods are advantageous in simulations of fluids in domains with free boundary. Special SPH methods have also been developed to simulate solids. However, there are situations where the matter ...