基于PINNs方法求解非定常Stokes方程

应用传统数值方法求解偏微分方程已有许多研究,例如有限元、有限差分、有限体积等方法.上述方法都需要在求解过程中生成网格对积分或者微分区域进行剖分,这在面对高维问题时,可使得求解难度大幅度增加,尤其是影响求解的效率及计算复杂度.随着硬件技术、计算机软件的发展,机器学习方法逐渐成为研究偏微分方程的可用工具之一,这主要得益于神经网络的应用.通过物理信息神经网络(Physics Informed Neural Networks,PINN),可以将物理规律的相关先验知识与深度学习相结合,从而对偏微分方程进行求解.使用PINNs求解Stokes问题,通过网络优化了真解与逼近解之间的误差,并给出了数值实验来反映方法的可行性.

PINNs算法及其在岩土工程中的应用研究

物理信息神经网络(PINNs)算法采用自动微分方法将偏微分方程直接嵌入神经网络中,从而实现对偏微分方程的智能求解,属于一种新型的无网格算法,具有收敛速度快和计算精度高等优点。PINNs不仅能够实现对偏微分方程求解,还能够对偏微分方程未知参数进行反演,因此对岩土工程复杂问题具有广泛的应用前景。为了验证PINNs算法在岩土工程领域的可行性,对连续排水边界条件下的一维固结理论进行求解和界面参数反演。计算结果表明,PINNs数值结果与解析解具有高度一致性,且界面参数反演结果准确,说明PINNs算法能够为岩土工程相关问题提供新的求解思路。

Helmholtz方程反问题的PINNS解法

文章利用基于机器学习的内嵌物理机理神经网络(PINNs)方法求解Helmholtz方程及其参数识别反问题.针对Helmholtz方程正问题,利用自动微分将Helmholtz方程嵌入进深度神经网络损失函数,通过最小化损失函数来优化深度神经网络,得到求解Helmholtz方程算法;针对未知参数p,k^(2)的参数识别反问题,通过附加测量数据,得出了参数p,k^(2)的求解算法;数值算例表明,PINNs方法求解Helmholtz方程及其参数识别反问题的算法是有效的.

基于多域物理信息神经网络的复合地层隧道掘进地表沉降预测

复合地层中盾构掘进诱发地表沉降的准确预测是隧道工程安全建设与施工决策的关键问题。基于隧道施工诱发地层变形机制构建隧道收敛变形与掘进位置的联系,并将其耦合至深度神经网络(deep neural network,简称DNN)框架,建立了预测盾构掘进诱发地层变形的物理信息神经网络(physics-informed neural network,简称PINN)模型。针对隧道上覆多个地层的地质特征,提出了多域物理信息神经网络(multi-physics-informed neural network,简称MPINN)模型,实现了在统一的框架内对不同地层的物理信息分区域表达。结果表明:MPINN模型高度还原了有限差分法的计算结果,可以准确预测复合地层中隧道开挖诱发的地表沉降;由于融入了物理机制,MPINN模型对隧道施工诱发地表沉降的问题具有普适性,可应用于不同地质和几何条件下隧道诱发地表沉降的预测;基于工程实测数据,提出的MPINN模型准确预测了监测断面的地表沉降曲线,可为复合地层下盾构掘进过程中地表沉降的预测预警提供参考。

带有PT对称势的非线性薛定谔方程的两类反问题

本文对带有PT对称势三阶五阶幂律非线性薛定谔方程提出了关于参数和势函数反演的两类反问题。对于参数反演问题,我们分别采用PINNs (Physics Informed Neural Networks)和传统的结合有限差分法与优化算法求解的方法进行比较。计算结果显示,在求解反问题时,传统方法每步参数优化需要数值求解非线性薛定谔方程,计算量较大。而PINNs的方法无需重复求解薛定谔方程,计算效率更高。对于PT对称势函数反演问题,通过在PINNs中嵌入自适应基函数,从而反演得到PT对称势。数值实验显示PINNs在算法计算反问题效率上优于传统微分数值求解和优化相结合的方法。

Physics-informed neural networks(PINNs)for fluidmechanics:a review

Despite the significant progress over the last 50 years in simulating flow problems using numerical discretization of the Navier–Stokes equations(NSE),we still cannot incorporate seamlessly noisy data into existing algorithms,mesh-generation is complex,and we cannot tackle high-dimensional problems governed by parametrized NSE.Moreover,solving inverse flow problems is often prohibitively expensive and requires complex and expensive formulations and new computer codes.Here,we review flow physics-informed learning,integrating seamlessly data and mathematical models,and implement them using physics-informed neural networks(PINNs).We demonstrate the effectiveness of PINNs for inverse problems related to three-dimensional wake flows,supersonic flows,and biomedical flows.

Prediction of Porous Media Fluid Flow with Spatial Heterogeneity Using Criss-Cross Physics-Informed Convolutional Neural Networks

Recent advances in deep neural networks have shed new light on physics,engineering,and scientific computing.Reconciling the data-centered viewpoint with physical simulation is one of the research hotspots.The physicsinformedneural network(PINN)is currently the most general framework,which is more popular due to theconvenience of constructing NNs and excellent generalization ability.The automatic differentiation(AD)-basedPINN model is suitable for the homogeneous scientific problem;however,it is unclear how AD can enforce fluxcontinuity across boundaries between cells of different properties where spatial heterogeneity is represented bygrid cells with different physical properties.In this work,we propose a criss-cross physics-informed convolutionalneural network(CC-PINN)learning architecture,aiming to learn the solution of parametric PDEs with spatialheterogeneity of physical properties.To achieve the seamless enforcement of flux continuity and integration ofphysicalmeaning into CNN,a predefined 2D convolutional layer is proposed to accurately express transmissibilitybetween adjacent cells.The efficacy of the proposedmethodwas evaluated through predictions of several petroleumreservoir problems with spatial heterogeneity and compared against state-of-the-art(PINN)through numericalanalysis as a benchmark,which demonstrated the superiority of the proposed method over the PINN.

双曲型方程激波捕捉的物理信息神经网络(PINN)算法

双曲型方程的数值求解算法研究一直是偏微分方程研究的热点,其中,双曲型方程的间断捕捉是难点。受物理信息神经网络(physics-informed neural networks,PINN)启发,构造了改进的PINN算法,近似求解双曲型方程的间断问题。将坐标构造的数据集作为神经网络的输入,将PINN算法中的损失函数作为训练输出值与参考解(基于细网格的熵相容格式数据)或准确解的误差值,通过网络优化,最小化损失函数,得到最优网络参数。最后用数值算例验证了算法的可行性,数值结果表明,本文算法能捕捉激波,分辨率高,且未产生伪振荡。

基于物理信息神经网络的天气衍生品定价研究

基于温度指数的天气衍生品定价研究是一个热点.拟应用物理信息神经网络(PINNs)以求解基于O-U过程的天气衍生品定价偏微分方程,对HDD看跌期权进行了数值模拟.改进了PINNs算法的采样点,调整了梯度下降算法、学习率、迭代次数、权重分配等以加快收敛速度和提升拟合效果.通过与MCMC仿真模拟和单侧有限差分求解方法对比发现基于PINNs的方法具有相当的精度和计算速度,证明了PINNs算法求解天气衍生品定价偏微分方程的可行性.

间断问题扩散正则化的PINN反问题求解算法

双曲守恒律方程间断问题的求解是该类方程数值求解问题研究的重点之一.采用PINN(physics-informed neural networks)求解双曲守恒律方程正问题时需要添加扩散项,但扩散项的系数很难确定,需要通过试算方法来得到,造成很大的计算浪费.为了捕捉间断并节约计算成本,对方程进行了扩散正则化处理,将正则化方程纳入损失函数中,使用守恒律方程的精确解或参考解作为训练集,学习出扩散系数,进而预测出不同时刻的解.该算法与PINN求解正问题方法相比,间断解的分辨率得到了提高,且避免了多次试算系数的麻烦.最后,通过一维和二维数值试验验证了算法的可行性,数值结果表明新算法捕捉间断能力更强、无伪振荡和抹平现象的产生,且所学习出的扩散系数为传统数值求解格式构造提供了依据.

基于物理信息驱动神经网络的三维初至波旅行时计算方法

在地震勘探中,初至波旅行时的精确求取是偏移成像和旅行时反演等处理技术的重要基础。基于程函方程的有限差分算法在地震波旅行时求取中展现出良好的效果,但需要付出巨大的计算成本,尤其是对多震源、高密度网格的旅行时计算。为此,提出了一种基于物理信息驱动神经网络(PINN)的三维程函方程旅行时求取算法,由三维程函方程及其物理条件信息构成损失函数,再通过最小化该损失函数训练神经网络,最终输出满足程函方程的旅行时结果。不同速度模型的数值模拟实验结果表明,所提方法相对于传统算法具有更高的计算效率和更高的精确度。

基于稀疏数据的流场结构重构方法研究

无论是基于数值模拟还是物理实验,高精度流场数据的获取数量都极为有限,并且往往伴随着高昂的成本.现有方法无法通过有限数据重构出更加精细的流场结构,这极大地制约了相关气/水动相关工程问题的设计精度与设计效率.物理信息驱动型神经网络框架的提出,使得传统数据驱动型神经网络无法处理稀疏的问题在一定程度上得到解决.文中以物理信息驱动型神经网络框架为基础,发展了基于稀疏数据的流场结构重构方法,通过耦合流场物理信息,利用少量数据训练神经网络并输出全流场数据.通过分析重构流场的水动力特性与涡脱落特性,揭示了物理信息驱动型神经网络的预测误差机理,讨论了该方法对不同流场结构的预测能力.结果表明,物理信息驱动型神经网络通过耦合NS方程,仅利用极其有限的流场数据即可实现对全流场的高精度重构,对流场涡结构也能实现较为精准捕捉.

Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving partial differential equations with sharp solutions

We consider solving the forward and inverse partial differential equations(PDEs)which have sharp solutions with physics-informed neural networks(PINNs)in this work.In particular,to better capture the sharpness of the solution,we propose the adaptive sampling methods(ASMs)based on the residual and the gradient of the solution.We first present a residual only-based ASM denoted by ASMⅠ.In this approach,we first train the neural network using a small number of residual points and divide the computational domain into a certain number of sub-domains,then we add new residual points in the sub-domain which has the largest mean absolute value of the residual,and those points which have the largest absolute values of the residual in this sub-domain as new residual points.We further develop a second type of ASM(denoted by ASMⅡ)based on both the residual and the gradient of the solution due to the fact that only the residual may not be able to efficiently capture the sharpness of the solution.The procedure of ASMⅡis almost the same as that of ASMⅠ,and we add new residual points which have not only large residuals but also large gradients.To demonstrate the effectiveness of the present methods,we use both ASMⅠand ASMⅡto solve a number of PDEs,including the Burger equation,the compressible Euler equation,the Poisson equation over an Lshape domain as well as the high-dimensional Poisson equation.It has been shown from the numerical results that the sharp solutions can be well approximated by using either ASMⅠor ASMⅡ,and both methods deliver much more accurate solutions than the original PINNs with the same number of residual points.Moreover,the ASMⅡalgorithm has better performance in terms of accuracy,efficiency,and stability compared with the ASMⅠalgorithm.This means that the gradient of the solution improves the stability and efficiency of the adaptive sampling procedure as well as the accuracy of the solution.Furthermore,we also employ the similar adaptive sampling technique for the data points of boundary conditions(BCs)if the sharpness of the solution is near the boundary.The result of the L-shape Poisson problem indicates that the present method can significantly improve the efficiency,stability,and accuracy.

有限差分法和PINN法求解微分方程的探讨

在工程实际中的许多问题最终都可以转化为微分方程。由于一些微分方程复杂性,这些方程求解通常具有一定的难度。随着计算机的迅速发展,使得这些方程可以数值求解。如何设计高效的微分方程数值解法尤其重要。微分方程数值解法通常包括有限差分、有限元、有限体积等。近年来基于深度学习的微分方程求解方法十分火热。本文对内嵌物理信息神经网络(PINN)方法进行探讨。我们用传统的有限差分法和PINN法对常微分两点边值问题和偏微分方程中的一类热传导方程进行数值求解,对比分析两种数值解法的优缺点。从数值实验结果中可以看出用PINN相对于传统有限差分法求解微分方程具有更好的精度和效率。

An artificial viscosity augmented physics-informed neural network for incompressible flow

Physics-informed neural networks(PINNs)are proved methods that are effective in solving some strongly nonlinear partial differential equations(PDEs),e.g.,Navier-Stokes equations,with a small amount of boundary or interior data.However,the feasibility of applying PINNs to the flow at moderate or high Reynolds numbers has rarely been reported.The present paper proposes an artificial viscosity(AV)-based PINN for solving the forward and inverse flow problems.Specifically,the AV used in PINNs is inspired by the entropy viscosity method developed in conventional computational fluid dynamics(CFD)to stabilize the simulation of flow at high Reynolds numbers.The newly developed PINN is used to solve the forward problem of the two-dimensional steady cavity flow at Re=1000 and the inverse problem derived from two-dimensional film boiling.The results show that the AV augmented PINN can solve both problems with good accuracy and substantially reduce the inference errors in the forward problem.

Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics

Material identification is critical for understanding the relationship between mechanical properties and the associated mechanical functions.However,material identification is a challenging task,especially when the characteristic of the material is highly nonlinear in nature,as is common in biological tissue.In this work,we identify unknown material properties in continuum solid mechanics via physics-informed neural networks(PINNs).To improve the accuracy and efficiency of PINNs,we develop efficient strategies to nonuniformly sample observational data.We also investigate different approaches to enforce Dirichlet-type boundary conditions(BCs)as soft or hard constraints.Finally,we apply the proposed methods to a diverse set of time-dependent and time-independent solid mechanic examples that span linear elastic and hyperelastic material space.The estimated material parameters achieve relative errors of less than 1%.As such,this work is relevant to diverse applications,including optimizing structural integrity and developing novel materials.

Deep convolutional Ritz method: parametric PDE surrogates without labeled data

The parametric surrogate models for partial differential equations(PDEs)are a necessary component for many applications in computational sciences,and the convolutional neural networks(CNNs)have proven to be an excellent tool to generate these surrogates when parametric fields are present.CNNs are commonly trained on labeled data based on one-to-one sets of parameter-input and PDE-output fields.Recently,residual-based deep convolutional physics-informed neural network(DCPINN)solvers for parametric PDEs have been proposed to build surrogates without the need for labeled data.These allow for the generation of surrogates without an expensive offline-phase.In this work,we present an alternative formulation termed deep convolutional Ritz method(DCRM)as a parametric PDE solver.The approach is based on the minimization of energy functionals,which lowers the order of the differential operators compared to residualbased methods.Based on studies involving the Poisson equation with a spatially parameterized source term and boundary conditions,we find that CNNs trained on labeled data outperform DCPINNs in convergence speed and generalization abilities.The surrogates generated from the DCRM,however,converge significantly faster than their DCPINN counterparts,and prove to generalize faster and better than the surrogates obtained from both CNNs trained on labeled data and DCPINNs.This hints that the DCRM could make PDE solution surrogates trained without labeled data possibly.

Variational inference in neural functional prior using normalizing flows: application to differential equation and operator learning problems

Physics-informed deep learning has recently emerged as an effective tool for leveraging both observational data and available physical laws.Physics-informed neural networks(PINNs)and deep operator networks(DeepONets)are two such models.The former encodes the physical laws via the automatic differentiation,while the latter learns the hidden physics from data.Generally,the noisy and limited observational data as well as the over-parameterization in neural networks(NNs)result in uncertainty in predictions from deep learning models.In paper“MENG,X.,YANG,L.,MAO,Z.,FERRANDIS,J.D.,and KARNIADAKIS,G.E.Learning functional priors and posteriors from data and physics.Journal of Computational Physics,457,111073(2022)”,a Bayesian framework based on the generative adversarial networks(GANs)has been proposed as a unified model to quantify uncertainties in predictions of PINNs as well as DeepONets.Specifically,the proposed approach in“MENG,X.,YANG,L.,MAO,Z.,FERRANDIS,J.D.,and KARNIADAKIS,G.E.Learning functional priors and posteriors from data and physics.Journal of Computational Physics,457,111073(2022)”has two stages:(i)prior learning,and(ii)posterior estimation.At the first stage,the GANs are utilized to learn a functional prior either from a prescribed function distribution,e.g.,the Gaussian process,or from historical data and available physics.At the second stage,the Hamiltonian Monte Carlo(HMC)method is utilized to estimate the posterior in the latent space of GANs.However,the vanilla HMC does not support the mini-batch training,which limits its applications in problems with big data.In the present work,we propose to use the normalizing flow(NF)models in the context of variational inference(VI),which naturally enables the mini-batch training,as the alternative to HMC for posterior estimation in the latent space of GANs.A series of numerical experiments,including a nonlinear differential equation problem and a 100-dimensional(100D)Darcy problem,are conducted to demonstrate that the NFs with full-/mini-batch training are able to achieve similar accuracy as the“gold rule”HMC.Moreover,the mini-batch training of NF makes it a promising tool for quantifying uncertainty in solving the high-dimensional partial differential equation(PDE)problems with big data.

隔室模型和深度学习模型对COVID-19的预测研究

该文以隔室模型作为主干网络,在基础的SEIRD隔室模型上,考虑疫苗对疫情的影响,增加疫苗接种隔室,形成优化的SEIRDV隔室模型。结合隔室模型的可解释性和神经网络的准确性,通过PINN神经网络对隔室模型的微分方程参数进行学习。重点关注感染隔室的数据,以解释疫情的动态变化和爆发机制,并对神经网络得到的微分方程参数进行合理性检验。

融合物理的神经网络方法在流场重建中的应用

神经网络融合物理先验知识能极大提高其拟合复杂变量的能力,其中融合神经网络和物理控制方程的物理融合神经网络模型(physical-informed neural network, PINN),赋予传统神经网络所不具备的先验知识和可解释性。结合课题组对PINN方法的研究和应用,本文介绍了融合N-S方程的PINN神经网络模型预测能力。首先借助三维超声速槽道湍流的直接数值计算数据,耦合神经网络和可压缩N-S方程,应用PINN方法对槽流的瞬时流场的物理量进行预测,并对瞬时量及其统计平均值与DNS对应结果进行对比来验证训练所获PINN模型的可靠性。其次,借助不可压缩圆柱绕流与三维可压缩槽道流动的计算数据,利用PINN模型进行了N-S控制方程待定系数与待定项的重建,结果显示其在重建流场流动信息的同时可逼近方程的待定系数。研究结果证实了PINN方法可为建立流动物理模型提供工具和算法支撑。