An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

CRANIODENTAL VARIATION OF MACAQUES ( Macaca ): SIZE,FUNCTION AND PHYLOGENY

In order to analyze skull variation in the genus Macaca ,seventy seven craniodental variables were taken from eleven species.They were first defined seven functional units comprising three anatomical regions.Twenty seven variables were finally selected to carry out the morphology of the whole skull.The data,organized in these ways,were examined to discover variations between and within the various species.The methods used were Principal Components Analysis (PCA) and Discriminant Function Analysis (DFA).PCAs of the functional units anatomical regions,and the whole skull provided similar,though not identical,separations of species clusters in both sexes separately.These differences in structure could be related to size,sexual dimorphism,diet,ecology,classification and phylogeny.The question of size should have been easy to settle.Unfortunately,this is not the case.In this study where the raw data are measurements of the specimens,the main differences should be size.However,the size differences seem to occur in both the first and second (independent) multivariate axes.In some analyses the size differences between the species are biggest and appear in the first axis.In other analyses it is the separation between the sexes (and these too are largely size) that are the biggest and appear in the first axis.Yet in other analyses,both of these size separations,though still orthogonal to one another,present in the combination of the first two axes.This certainly implies that a single axis of body size is not present and that shape differences have not been isolated form size differences.It also implies that sexual dimorphism is a complex matter.As a result,the question of the relationships between the species is therefore also complex.One cluster of species that includes M fascicularis,M sinica and M radiata was significantly isolated from all others regardless of level of analysis.This relationship is quite different from that proposed on the anatomy of the reproductive organs (Delson,1980;Fooden,1976,1980).

Adaptive Control and Function Projective Synchronization in 2D Discrete-Time Chaotic Systems

This study addresses the adaptive control and function projective synchronization problems between 2D Rulkov discrete-time system and Network discrete-time system. Based on backstepping design with three controllers, a systematic, concrete and automatic scheme is developed to investigate the function projective synchronization of discretetime chaotic systems. In addition, the adaptive control function is applied to achieve the state synchronization of two discrete-time systems. Numerical results demonstrate the effectiveness of the proposed control scheme.

Adaptive predictive functional control based on Takagi-Sugeno model and its application to pH process

In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive functional control(AFPFC) scheme for multivariable nonlinear systems was proposed.Firstly,multivariable nonlinear systems were described based on Takagi-Sugeno(T-S) fuzzy models;assuming that the antecedent parameters of T-S models were kept,the consequent parameters were identified on-line by using the weighted recursive least square(WRLS) method.Secondly,the identified T-S models were linearized to be time-varying state space model at each sampling instant.Finally,by using linear predictive control technique the analysis solution of the optimal control law of AFPFC was established.The application results for pH neutralization process show that the absolute error between the identified T-S model output and the process output is smaller than 0.015;the tracking ability of the proposed AFPFC is superior to that of non-AFPFC(NAFPFC) for pH process without disturbances,the overshoot of the effluent pH value of AFPFC with disturbances is decreased by 50% compared with that of NAFPFC;when the process parameters of AFPFC vary with time the integrated absolute error(IAE) performance index still retains to be less than 200 compared with that of NAFPFC.

An effective quadrilateral mesh adaptation

Accuracy of a simulation strongly depends on the grid quality. Here, quality means orthogonality at the boundaries and quasi-orthogonality within the critical regions, smoothness, bounded aspect ratios and solution adaptive behaviour. It is not recommended to refine the parts of the domain where the solution shows little variation. It is desired to concentrate grid points and cells in the part of the domain where the solution shows strong gradients or variations. We present a simple, effective and com- putationally efficient approach for quadrilateral mesh adaptation. Several numerical examples are presented for supporting our claim.

Numerical simulation on the adaptation of forms in trabecular bone to mechanical disuse and basic multi-cellular unit activation threshold at menopause

The objective of this paper is to identify the effects of mechanical disuse and basic multi-cellular unit （BMU） activation threshold on the form of trabecular bone during menopause. A bone adaptation model with mechanical- biological factors at BMU level was integrated with finite element analysis to simulate the changes of trabecular bone structure during menopause. Mechanical disuse and changes in the BMU activation threshold were applied to the model for the period from 4 years before to 4 years after menopause. The changes in bone volume fraction, trabecular thickness and fractal dimension of the trabecular structures were used to quantify the changes of trabecular bone in three different cases associated with mechanical disuse and BMU activation threshold. It was found that the changes in the simulated bone volume fraction were highly correlated and consistent with clinical data, and that the trabecular thickness reduced signi-ficantly during menopause and was highly linearly correlated with the bone volume fraction, and that the change trend of fractal dimension of the simulated trabecular structure was in correspondence with clinical observations. The numerical simulation in this paper may help to better understand the relationship between the bone morphology and the mecha-nical, as well as biological environment; and can provide a quantitative computational model and methodology for the numerical simulation of the bone structural morphological changes caused by the mechanical environment, and/or the biological environment.

PARALLEL ADAPTIVELY MODIFIED CHARACTERISTIC BASIS FUNCTION METHOD BASED ON STATIC LOAD BALANCE

Characteristic Basis Function Method (CBFM) is a novel approach for analyzing the ElectroMagnetic (EM) scattering from electrically large objects. Based on dividing the studied object into small blocks, the CBFM is suitable for parallel computing. In this paper, a static load balance parallel method is presented by combining Message Passing Interface (MPI) with Adaptively Modified CBFM (AMCBFM). In this method, the object geometry is partitioned into distinct blocks, and the serial number of blocks is sent to related nodes according to a certain rule. Every node only needs to calculate the information on local blocks. The obtained results confirm the accuracy and efficiency of the proposed method in speeding up solving large electrical scale problems.

Applications of Fractional Lower Order Time-frequency Representation to Machine Bearing Fault Diagnosis

The machinery fault signal is a typical non-Gaussian and non-stationary process. The fault signal can be described by SaS distribution model because of the presence of impulses.Time-frequency distribution is a useful tool to extract helpful information of the machinery fault signal. Various fractional lower order(FLO) time-frequency distribution methods have been proposed based on fractional lower order statistics, which include fractional lower order short time Fourier transform(FLO-STFT), fractional lower order Wigner-Ville distributions(FLO-WVDs), fractional lower order Cohen class time-frequency distributions(FLO-CDs), fractional lower order adaptive kernel time-frequency distributions(FLO-AKDs) and adaptive fractional lower order time-frequency auto-regressive moving average(FLO-TFARMA) model time-frequency representation method.The methods and the exiting methods based on second order statistics in SaS distribution environments are compared, simulation results show that the new methods have better performances than the existing methods. The advantages and disadvantages of the improved time-frequency methods have been summarized.Last, the new methods are applied to analyze the outer race fault signals, the results illustrate their good performances.

Dental Variation Among Asian Colobines, with Specific Reference to the Macaques on the Same Continent

In order to discern dental morphometric variations among the Asian colobines, residuals of the colobines, derived from allometric baselines formed by the Asian macaques （Macaca）, were analyzed with Principal Components Analysis and Euclidean Distances. Results indicated that the widely accepted view that the colobines possess relatively smaller front teeth than the macaques is only the case for the first incisors. The colobines show relatively smaller molars than the macaques. Such profiles may be related to the differences in dietary preferences between the two major groups of the Asian Old World monkeys. The magnitude of such differences is not as great as usullay assumed for the two groups that contain both African and Asian taxa. In other words, the two Asian cercopithecoid groups may have homogenously been shaped by the tectonic modifications and climate alterations in the past five million years. There exist marked differences among the Asian colobines when each of the genera is compared with macaques; the dental profile reflects not only the variation in geographic distribution but also in phylogenetic divergence. Thus, the snub-nosed monkeys （Rhinopithecus） and the gray langurs （Semnopithecus） are characterized by relatively larger molars than the other colobines - larger even than those of the macaques. The differences among Asian colobines, depicted by Euclidean Distances, seems to reflect the relationship of the phylogeny and evolution between colobines and cercopithecines.

Trajectory optimization of a reentry vehicle based on artificial emotion memory optimization

The trajectory optimization of an unpowered reentry vehicle via artificial emotion memory optimization(AEMO)is discussed.Firstly,reentry dynamics are established based on multiple constraints and parameterized control variables with finite dimensions are designed.If the constraint is not satisfied,a distance measure and an adaptive penalty function are used to address this scenario.Secondly,AEMO is introduced to solve the trajectory optimization problem.Based on the theories of biology and cognition,the trial solutions based on emotional memory are established.Three search strategies are designed for realizing the random search of trial solutions and for avoiding becoming trapped in a local minimum.The states of the trial solutions are determined according to the rules of memory enhancement and forgetting.As the iterations proceed,the trial solutions with poor quality will gradually be forgotten.Therefore,the number of trial solutions is decreased,and the convergence of the algorithm is accelerated.Finally,a numerical simulation is conducted,and the results demonstrate that the path and terminal constraints are satisfied and the method can realize satisfactory performance.

A useful electroencephalography(EEG) marker of brain plasticity: delta waves

Plasticity is a natural property of living organisms that is crucial for adaptation and evolution.Over the last decades,the availability of sophisticated neuroimaging techniques（in particular,functional magnetic resonance imaging（f MRI）,and transcranial magnetic stimulation（TMS））,has made it possible to explore in vivo the on-line functioning of brain and its plasticity.However,

Strategy of changing cracking furnace feedstock based on improved group search optimization

The scheduling process of cracking furnace feedstock is important in an ethylene plant. In this paper it is described as a constraint optimization problem. The constraints consist of the cycle of operation, maximum tube metal temperature, process time of each feedstock, and flow rate. A modified group search optimizer is proposed to deal with the optimization problem. Double fitness values are defined for every group. First, the factor of penalty function should be changed adaptively by the ratio of feasible and general solutions. Second, the "excellent" infeasible solution should be retained to guide the search. Some benchmark functions are used to evaluate the new algorithm. Finally, the proposed algorithm is used to optimize the scheduling process of cracking furnace feedstock. And the optimizing result is obtained.

A NOVEL TEMPORAL ERROR CONCEALMENT METHOD BASED ON FUZZY REASONING FOR H.264

In this paper,a fuzzy reasoning based temporal error concealment method is proposed. The basic temporal error concealment is implemented by estimating Motion Vector (MV) of the lost MacroBlock (MB) from its neighboring MVs. Which MV is the most proper one is evaluated by some criteria. Generally,two criteria are widely used,namely Side Match Distortion (SMD) and Sum of Absolute Difference (SAD) of corresponding MV. However,each criterion could only partly describe the status of lost block. To accomplish the judgement more accurately,the two measures are considered together. Thus a refined measure based on fuzzy reasoning is adopted to balance the effects of SMD and SAD. Terms SMD and SAD are regarded as fuzzy input and the term ‘similarity’ as output to complete fuzzy reasoning. Result of fuzzy reasoning represents how the tested MV is similar to the original one. And k-means clustering technique is performed to define the membership function of input fuzzy sets adaptively. According to the experimental results,the concealment based on new measure achieves better performance.

Adaptive neuro-fuzzy interface system for gap acceptance behavior of right-turning vehicles at partially controlled T-intersections

Gap acceptance theory is broadly used for evaluating unsignalized intersections in developed coun tries. Intersections with no specific priority to any move ment, known as uncontrolled intersections, are common in India. Limited priority is observed at a few intersections, where priorities are perceived by drivers based on geom etry, traffic volume, and speed on the approaches of intersection. Analyzing such intersections is complex because the overall traffic behavior is the result of drivers, vehicles, and traffic flow characteristics. Fuzzy theory has been widely used to analyze similar situations. This paper describes the application of adaptive neurofuzzy interface system （ANFIS） to the modeling of gap acceptance behavior of rightturning vehicles at limited priority Tintersections （in India, vehicles are driven on the left side of a road）. Field data are collected using video cameras at four Tintersections having limited priority. The data extracted include gap/lag, subject vehicle type, conflicting vehicle type, and driver＇s decision （accepted/rejected）. ANFIS models are developed by using 80 % of the extracted data （total data observations for major road right turning vehicles are 722 and 1,066 for minor road right turning vehicles） and remaining are used for model vali dation. Four different combinations of input variables are considered for major and minor road right turnings sepa rately. Correct prediction by ANFIS models ranges from 75.17 % to 82.16 % for major road right turning and 87.20 % to 88.62 % for minor road right turning. Themodels developed in this paper can be used in the dynamic estimation of gap acceptance in traffic simulation models.

Modeling and robust adaptive control for a coaxial twelve-rotor UAV

Compared with the quad-rotor unmanned aerial vehicle (UAV), the coaxial twelve-rotor UAV has stronger load carrying capacity, higher driving ability and stronger damage resistance. This paper focuses on its robust adaptive control. First, a mathematical model of a coaxial twelve-rotor is established. Aiming at the problem of model uncertainty and external disturbance of the coaxial twelve-rotor UAV, the attitude controller is innovatively adopted with the combination of a backstepping sliding mode controller (BSMC) and an adaptive radial basis function neural network (RBFNN). The BSMC combines the advantages of backstepping control and sliding mode control, which has a simple design process and strong robustness. The RBFNN as an uncertain observer, can effectively estimate the total uncertainty. Then the stability of the twelve-rotor UAV control system is proved by Lyapunov stability theorem. Finally, it is proved that the robust adaptive control strategy presented in this paper can overcome model uncertainty and external disturbance effectively through numerical simulation and prototype of twelve-rotor UAV tests.

Modeling and Solving Human Arm’s Posture in Reachablity Analysis

Reachability is a key criterion in maintenance design, and human arm is the main object in reachability analysis. The human arm＇s DOF is reduced, and applying military standards and human physiological constraints, the simplified arm model of 7-DOF using D-H method is built up. Particle Swarm Optimization （PSO） is used to acquire the shoulder, arm and hand posture with adaptive fitness function. A detailed reachability analysis is accomplished for disassembling the bolts from crank shaft is given as an example to validate the feasibility of using teachability analysis on maintenance design.

Energy Management Optimization Based on Aging Adaptive Functional State Model of Battery for Internal Combustion Engine Vehicles

This paper presents an energy management optimization system based on an adaptive functional state model of battery aging for internal combustion engine vehicles(ICEVs).First,the functional characteristics of batteries in ICEVs are investigated.Then,an adaptive functional state model is proposed to represent battery aging throughout the entire battery service life.A battery protection scheme is developed,including over-discharge and graded over-current protection to improve battery safety.A model-based energy management strategy is synthesized to comprehensively optimize fuel economy,battery life preservation,and vehicle performance.The performance of the proposed scheme was examined under comprehensive test scenarios based on field and bench tests.The results show that the proposed energy management algorithm can effectively improve fuel economy.

Learning Specialized Activation Functions for Physics-Informed Neural Networks

Physics-informed neural networks(PINNs)are known to suffer from optimization difficulty.In this work,we reveal the connection between the optimization difficulty of PINNs and activation functions.Specifically,we show that PINNs exhibit high sensitivity to activation functions when solving PDEs with distinct properties.Existing works usually choose activation functions by inefficient trial-and-error.To avoid the inefficient manual selection and to alleviate the optimization difficulty of PINNs,we introduce adaptive activation functions to search for the optimal function when solving different problems.We compare different adaptive activation functions and discuss their limitations in the context of PINNs.Furthermore,we propose to tailor the idea of learning combinations of candidate activation functions to the PINNs optimization,which has a higher requirement for the smoothness and diversity on learned functions.This is achieved by removing activation functions which cannot provide higher-order derivatives from the candidate set and incorporating elementary functions with different properties according to our prior knowledge about the PDE at hand.We further enhance the search space with adaptive slopes.The proposed adaptive activation function can be used to solve different PDE systems in an interpretable way.Its effectiveness is demonstrated on a series of benchmarks.Code is available at https://github.com/LeapLabTHU/AdaAFforPINNs.

Perfect Adaptation of General Nonlinear Systems

Perfect adaptation describes the ability of a biological system to restore its biological function precisely to the pre-perturbation level after being affected by the environmental disturbances.Mathematically,a biological system with perfect adaptation can be modelled as an input-output nonlinear system whose output,usually determining the biological function,is asymptotically stable under all input disturbances concerned.In this paper,a quite general input-output mathematical model is employed and the 'functional' of biological function(FBF)- output Lyapunov function- is explored to investigate its perfect adaptation ability.Sufficient condition is established for the systems with FBF to achieve perfect adaptation.Then a sufficient and necessary condition is obtained for the linear systems to possess an output Lyapunov function.Furthermore,it is shown that the 'functional'of receptors activity exists in the perfect adaptation model of E.coh chemotaxis.Different with the existing mathematical surveys on perfect adaptation,most of which are based on the standpoint of control theory,we first investigate this problem using ways of nonlinear systems analysis.

Solving nonlinear soliton equations using improved physics-informed neural networks with adaptive mechanisms

Partial differential equations(PDEs)are important tools for scientific research and are widely used in various fields.However,it is usually very difficult to obtain accurate analytical solutions of PDEs,and numerical methods to solve PDEs are often computationally intensive and very time-consuming.In recent years,Physics Informed Neural Networks(PINNs)have been successfully applied to find numerical solutions of PDEs and have shown great potential.All the while,solitary waves have been of great interest to researchers in the field of nonlinear science.In this paper,we perform numerical simulations of solitary wave solutions of several PDEs using improved PINNs.The improved PINNs not only incorporate constraints on the control equations to ensure the interpretability of the prediction results,which is important for physical field simulations,in addition,an adaptive activation function is introduced.By introducing hyperparameters in the activation function to change the slope of the activation function to avoid the disappearance of the gradient,computing time is saved thereby speeding up training.In this paper,the m Kd V equation,the improved Boussinesq equation,the Caudrey–Dodd–Gibbon–Sawada–Kotera equation and the p-g BKP equation are selected for study,and the errors of the simulation results are analyzed to assess the accuracy of the predicted solitary wave solution.The experimental results show that the improved PINNs are significantly better than the traditional PINNs with shorter training time but more accurate prediction results.The improved PINNs improve the training speed by more than 1.5 times compared with the traditional PINNs,while maintaining the prediction error less than 10~(-2)in this order of magnitude.