GAN-based Data Mapping for Model Adaptation
Explore the source record for details and available documents.
Mathematics and Computing, Computer science↗
SEARCH · Search NASA
Results for “adaptive model”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Adaptive mesh refinement and turbulence modeling
Adaptive mesh refinement is shown to be essential in turbulence modeling. Adaptive Mesh Refinement (AMR) is a more computationally efficient means to obtain a discrete approximation to a continuous boundary value problem of a specified accuracy than classic isotropic grid refinement. Previous application of this methodology in the assessment of turbulence models suggested that the field variable solution on the interior of the domain was more sensitive to grid refinement than would be suggested by the sensitivity to grid refinement of surface quantities. Revisiting challenging high speed flow field revealed that issues believed to be fundamental turbulence model issues appear to be related to an under-resolved boundary layer edge – a flow detail previously considered unimportant.
Hypersonics↗
Control of a nonlinear plant with two level input using a tabular adaptive-predictive model.
Tabular adaptive predictive model for trajectory following control of nonlinear plant with switched two-level input
NONLINEAR SYSTEM↗
Hybrid Adaptive Flight Control with Model Inversion Adaptation
This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.
Nguyen, Nhan↗
Guidelines for Field Measurements for Model Validation: Adaptive Protection and Validated MODels to Enable Deployment of High Penetrations of Solar PV (PV-MOD) [Slides]
This report develops guidance for monitoring, collection, and selection of field measurements for model validation.
14 SOLAR ENERGY↗
Statistical behaviour of adaptive multilevel splitting algorithms in simple models
Adaptive multilevel splitting algorithms have been introduced rather recently for estimating tail distributions in a fast and efficient way. In particular, they can be used for computing the so-called reactive trajectories corresponding to direct transitions from one metastable state to another. The algorithm is based on successive selection–mutation steps performed on the system in a controlled way. It has two intrinsic parameters, the number of particles/trajectories and the reaction coordinate used for discriminating good or bad trajectories. We investigate first the convergence in law of the algorithm as a function of the timestep for several simple stochastic models. Second, we consider the average duration of reactive trajectories for which no theoretical predictions exist. The most important aspect of this work concerns some systems with two degrees of freedom. They are studied in detail as a function of the reaction coordinate in the asymptotic regime where the number of trajectories goes to infinity. We show that during phase transitions, the statistics of the algorithm deviate significatively from known theoretical results when using non-optimal reaction coordinates. In this case, the variance of the algorithm is peaking at the transition and the convergence of the algorithm can be much slower than the usual expected central limit behaviour. The duration of trajectories is affected as well. Moreover, reactive trajectories do not correspond to the most probable ones. Such behaviour disappears when using the optimal reaction coordinate called committor as predicted by the theory. We finally investigate a three-state Markov chain which reproduces this phenomenon and show logarithmic convergence of the trajectory durations.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Model of the adaptive behavior of the human operator in response to a sudden change in the control situation.
Adaptive model of human operator control strategy in response to sudden changes in plant dynamics and transient disturbances
Bekey, G. A.↗
A Nonlinear Dynamic Inversion Predictor-Based Model Reference Adaptive Controller for a Generic Transport Model
Presented here is a Predictor-Based Model Reference Adaptive Control (PMRAC) architecture for a generic transport aircraft. At its core, this architecture features a three-axis, non-linear, dynamic-inversion controller. Command inputs for this baseline controller are provided by pilot roll-rate, pitch-rate, and sideslip commands. This paper will first thoroughly present the baseline controller followed by a description of the PMRAC adaptive augmentation to this control system. Results are presented via a full-scale, nonlinear simulation of NASA s Generic Transport Model (GTM).
Campbell, Stefan F.↗
An Adaptive Geometry-Free Thermo-Mechanical Model for Directed Energy Deposition Process Modeling
This presentation describes a novel, geometry-free thermo-mechanical model with adaptive subdomain con- struction to accurately predict the thermal conditions, distortions, and residual stresses throughout the directed energy deposition (DED) process. A novel finite element workflow is designed to con- duct the numerical analysis, based on the multi-app and data transfer capabilities in the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE). Unlike with traditional methods, the part geometry in this model is not predefined. Instead, it is a combined effect of the processing parameters and material properties. At each time step, the model utilizes a subdomain construction paradigm to model the material deposition. A specialized mesh adaptivity scheme is incorporated to provide an accurate prediction while reducing the overall computational cost. The results generated by the proposed model show general agreement with the experimental measurements for the single track scan with varying processing parameters and demonstrate reasonable predictions for higher material buildups.
36 MATERIALS SCIENCE↗
Resilience of Adapting Networks: Results from a Stylized Infrastructure Model.
Adaptation is believed to be a source of resilience in systems. It has been difficult to measure the contribution of adaptation to resilience, unlike other resilience mechanisms such as restoration and recovery. One difficulty comes from treating adaptation as a deus ex machina that is interjected after a disruption. This provides no basis for bounding possible adaptive responses. We can bracket the possible effects of adaptation when we recognize that it occurs continuously, and is in part responsible for the current system’s properties. In this way the dynamics of the system’s pre-disruption structure provides information about post-disruption adaptive reaction. Seen as an ongoing process, adaptation has been argued to produce “robust-yet-fragile” systems. Such systems perform well under historical stresses but become committed to specific features of those stresses in a way that makes them vulnerable to system-level collapse when those features change. In effect adaptation lessens the cost of disruptions within a certain historical range, at the expense of increased cost from disruptions outside that range. Historical adaptive responses leave a signature in the structure of the system. Studies of ecological networks have suggested structural metrics that pick out systemic resilience in the underlying ecosystems. If these metrics are generally reliable indicators of resilience they provide another strategy for gaging adaptive resilience. To progress in understanding how the process of adaptation and the property of resilience interrelate in infrastructure systems, we pose some specific questions: Does adaptation confer resilience?; Does it confer resilience to novel shocks as well, or does it tune the system to fragility?; Can structural features predict resilience to novel shocks?; Are there policies or constraints on the adaptive process that improve resilience?.
97 MATHEMATICS AND COMPUTING↗
Modeling the behavioral substrates of associate learning and memory - Adaptive neural models
Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive/learning systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Furthermore, each model can find the most nonredundant and earliest predictor of reinforcement. The behavior of the models accounts for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well the models fit empirical data from various animal learning paradigms.
Lee, Chuen-Chien↗
Model reference adaptive control for systems with time varying model commands
Model reference adaptive control is applied to linear time invariant systems for the case of arbitrary time varying model commands. Asymptotic stability is guaranteed, provided that the output stabilized transfer matrix is strictly positive real. Only output measurements are needed. Neither perfect model following nor explicit parameter identification is required. Simulations show the scheme to be capable of guaranteeing stability when the model inputs are time varying.
Abida, L.↗
Analyzing the Adaptive Mesh Refinement (AMR) Characteristics of a High-Order 2D Cubed-Sphere Shallow-Water Model
Adaptive mesh refinement (AMR) is a technique that has been featured only sporadically in atmospheric science literature. This study aims to demonstrate the utility of AMR for simulating atmospheric flows. Several test cases are implemented in a 2D shallow-water model on the sphere using the Chombo-AMR dynamical core. This high-order finite-volume model implements adaptive refinement in both space and time on a cubed-sphere grid using a mapped-multiblock mesh technique. The tests consist of the passive advection of a tracer around moving vortices, a steady-state geostrophic flow, an unsteady solid-body rotation, a gravity wave impinging on a mountain, and the interaction of binary vortices. Both static and dynamic refinements are analyzed to determine the strengths and weaknesses of AMR in both complex flows with small-scale features and large-scale smooth flows. The different test cases required different AMR criteria, such as vorticity or height-gradient based thresholds, in order to achieve the best accuracy for cost. The simulations show that the model can accurately resolve key local features without requiring global high-resolution grids. The adaptive grids are able to track features of interest reliably without inducing noise or visible distortions at the coarse–fine interfaces. Finally and furthermore, the AMR grids keep any degradations of the large-scale smooth flows to a minimum.
54 ENVIRONMENTAL SCIENCES↗
Input error versus output error model reference adaptive control
Algorithms for model reference adaptive control were developed in recent years, and their stability and convergence properties have been investigated. Typical algorithms in continuous time involve strictly positive real conditions on the reference model, while similar discrete time algorithms do not require such conditions. It is shown how algorithms differ by the use of an input error versus an output error, and present a continuous time input error adaptive control algorithm which does not involve SPR conditions. The connections with other schemes are discussed. The input error scheme has general stability and ocnvergence properties that are similar to the output error scheme. However, analysis using averaging methods reveals some preferable convergence properties of the input error scheme. Several other advantages are also discussed.
Bodson, Marc↗
Nonlinear and Digital Man-machine Control Systems Modeling
An adaptive modeling technique is examined by which controllers can be synthesized to provide corrective dynamics to a human operator's mathematical model in closed loop control systems. The technique utilizes a class of Liapunov functions formulated for this purpose, Liapunov's stability criterion and a model-reference system configuration. The Liapunov function is formulated to posses variable characteristics to take into consideration the identification dynamics. The time derivative of the Liapunov function generate the identification and control laws for the mathematical model system. These laws permit the realization of a controller which updates the human operator's mathematical model parameters so that model and human operator produce the same response when subjected to the same stimulus. A very useful feature is the development of a digital computer program which is easily implemented and modified concurrent with experimentation. The program permits the modeling process to interact with the experimentation process in a mutually beneficial way.
Mekel, R.↗
Adaptive language model training for molecular design
Abstract The vast size of chemical space necessitates computational approaches to automate and accelerate the design of molecular sequences to guide experimental efforts for drug discovery. Genetic algorithms provide a useful framework to incrementally generate molecules by applying mutations to known chemical structures. Recently, masked language models have been applied to automate the mutation process by leveraging large compound libraries to learn commonly occurring chemical sequences (i.e., using tokenization) and predict rearrangements (i.e., using mask prediction). Here, we consider how language models can be adapted to improve molecule generation for different optimization tasks. We use two different generation strategies for comparison, fixed and adaptive. The fixed strategy uses a pre-trained model to generate mutations; the adaptive strategy trains the language model on each new generation of molecules selected for target properties during optimization. Our results show that the adaptive strategy allows the language model to more closely fit the distribution of molecules in the population. Therefore, for enhanced fitness optimization, we suggest the use of the fixed strategy during an initial phase followed by the use of the adaptive strategy. We demonstrate the impact of adaptive training by searching for molecules that optimize both heuristic metrics, drug-likeness and synthesizability, as well as predicted protein binding affinity from a surrogate model. Our results show that the adaptive strategy provides a significant improvement in fitness optimization compared to the fixed pre-trained model, empowering the application of language models to molecular design tasks.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗