NUNet: Deep Learning for Non-Uniform Super-Resolution of Turbulent Flows

Obiols-Sales, Octavi; Vishnu, Abhinav; Malaya, Nicholas; Chandramowlishwaran, Aparna

doi:10.48550/arxiv.2203.14154

Cited by 2 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[10] proposed a local single-agent RL approach whereby the agent makes a decision for one randomly-selected element at each step. At training time, the global solution is updated every time a Other work at the intersection of FEM and deep learning include reinforcement learning for generating a fixed (nonadaptive) mesh [26], unsupervised clustering for marking and p-refinement [33], and supervised learning for target resolution prediction [23], error estimation [37], and mesh movement [30].…”

Section: Related Workmentioning

confidence: 99%

“…Traditional methods for AMR rely on estimating local refinement indicators (e.g., local error [43]) and heuristic marking strategies (e.g., greedy error-based marking) [4,7]. Recent data-driven methods for mesh refinement apply supervised learning to learn a fast neural network estimator of the solution from a fixed dataset of pre-generated high-resolution solutions [23,37]. However, greedy strategies based on local information cannot produce an optimal sequence of anticipatory refinement decisions in general, as they do not have sufficient information about features that may occur at subsequent time steps, while supervised methods do not directly optimize a given long-term objective.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement

Yang¹,

Mittal²,

Dzanic³

et al. 2022

Preprint

View full text Add to dashboard Cite

Adaptive mesh refinement (AMR) is necessary for efficient finite element simulations of complex physical phenomenon, as it allocates limited computational budget based on the need for higher or lower resolution, which varies over space and time. We present a novel formulation of AMR as a fully-cooperative Markov game, in which each element is an independent agent who makes refinement and de-refinement choices based on local information. We design a novel deep multi-agent reinforcement learning (MARL) algorithm called Value Decomposition Graph Network (VDGN), which solves the two core challenges that AMR poses for MARL: posthumous credit assignment due to agent creation and deletion, and unstructured observations due to the diversity of mesh geometries. For the first time, we show that MARL enables anticipatory refinement of regions that will encounter complex features at future times, thereby unlocking entirely new regions of the error-cost objective landscape that are inaccessible by traditional methods based on local error estimators. Comprehensive experiments show that VDGN policies significantly outperform error threshold-based policies in global error and cost metrics. We show that learned policies generalize to test problems with physical features, mesh geometries, and longer simulation times that were not seen in training. We also extend VDGN with multi-objective optimization capabilities to find the Pareto front of the tradeoff between cost and error.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement

Yang¹,

Mittal²,

Dzanic³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition, many surrogate modeling applications are usually designed for specific tasks and cannot easily be transferred to a more general built environment scenario (Fang et al 2019;Tian et al 2022). In other recent built environment studies, deep learning models have been applied together with CFD simulations to reconstruct high resolution flow fields starting from a low resolution input, either CFD data (Obiols-Sales et al 2022;Pourbagian and Ashrafizadeh 2022) or sensor measurements data (Wei and Ooka 2023). This technique is typically referred to as super-resolution, and different deep learning architectures were applied, including physics-informed networks (Wei and Ooka 2023).…”

Section: Introductionmentioning

confidence: 99%

Deep learning to develop zero-equation based turbulence model for CFD simulations of the built environment

Calzolari,

Liu

2023

Build. Simul.

View full text Add to dashboard Cite

This study aims to improve the accuracy and speed of predictions for thermal comfort and air quality in built environments by creating a coupled framework between computational fluid dynamics (CFD) simulations and deep learning models. The coupling approach is showcased by the development of a data-driven turbulence model. The new turbulence model is built using a deep learning neural network, whose mapping structure is based on a zero-equation turbulence model for built environment simulations, and is coupled with the CFD software OpenFOAM to create a hybrid framework. The neural network is a standard shallow multi-layer perceptron. The number of hidden layers and nodes per layer was optimized using Bayesan optimization algorithm. The framework is trained on an indoor environment case study, as well as tested on an indoor office simulation and an outdoor building array simulation. Results show that the deep learning based turbulence model is more robust and faster than traditional two-equation Reynolds average Navier-Stokes (RANS) turbulence models, while maintaining a similar level of accuracy. The model also outperforms the standard algebraic zero-equation model due to its superior ability to generalize to various flow scenarios. Despite some challenges, namely the mapping constraint, the limited training dataset size and the source of generation of training data, the hybrid framework demonstrates the viability of the coupling technique and serves as a starting point for future development of more reliable and advanced models.

show abstract

NUNet: Deep Learning for Non-Uniform Super-Resolution of Turbulent Flows

Cited by 2 publications

References 0 publications

Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement

Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement

Deep learning to develop zero-equation based turbulence model for CFD simulations of the built environment

Contact Info

Product

Resources

About