Physics guided neural networks for modelling of non-linear dynamics
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97 MATHEMATICS AND COMPUTING↗
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Results for “ordinary differential equation”
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.
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Theoretical modeling of hepatitis C acute infection in liver-humanized mice support pre-clinical assessment of candidate viruses for controlled-human-infection studies
Designing and carrying out a controlled human infection (CHI) model for hepatitis C virus (HCV) is critical for vaccine development. However, key considerations for a CHI model protocol include understanding of the earliest viral-host kinetic events during the acute phase and susceptibility of the viral isolate under consideration for use in the CHI model to antiviral treatment before any infections in human volunteers can take place. Humanized mouse models lack adaptive immune responses but provide a unique opportunity to obtain quantitative understanding of early HCV kinetics and develop mathematical models to further understand viral and innate immune response dynamics during acute HCV infection. We show that the models reproduce the measured HCV kinetics in humanized mice, which are consistent with early acute HCV-host dynamics in immunocompetent chimpanzees. Our findings suggest that humanized mice are well-suited to support development of a CHI model. In-silico and in-vivo modeling estimates provide a starting point to characterize candidate viruses for testing in CHI model studies.
Agent-based modeling↗
Acceleration of Power System Dynamic Simulations Using a Deep Equilibrium Layer and Neural ODE Surrogate
The dominant paradigm for power system dynamic simulation is to build system-level simulations by combining physics-based models of individual components. The sheer size of the system along with the rapid integration of inverter-based resources exacerbates the computational burden of running time domain simulations. Here, in this paper, we propose a data-driven surrogate model based on implicit machine learningspecifically deep equilibrium layers and neural ordinary differential equationsto learn a reduced order model of a portion of the full underlying system. The data-driven surrogate achieves similar accuracy and reduction in simulation time compared to a physics-based surrogate, without the constraint of requiring detailed knowledge of the underlying dynamic models. This work also establishes key requirements needed to integrate the surrogate into existing simulation workflows; the proposed surrogate is initialized to a steady state operating point that matches the power flow solution by design.
Neural ordinary differential equations↗
Analytic solutions of the DGLAP evolution and theoretical uncertainties
The energy dependence for the singlet sector of Parton Distributions Functions (PDFs) is described by an entangled pair of ordinary linear differential equations. Although there are no exact analytic solutions, it is possible to provide approximated results depending on the assumptions and the methodology adopted. These results differ in their sub-leading, neglected terms and ultimately they are associated with different treatments of the theoretical uncertainties. In this work, a novel analytic approach in Mellin space is presented and a new methodology for obtaining closed and exponentiated analytic solutions is devised. Different results for the DGLAP evolution at Next-Leading-Order are compared, discussing advantages and disadvantages for each solution. The generalizations to higher orders are addressed.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Quantification of early nonpharmaceutical interventions aimed at slowing transmission of Coronavirus Disease 2019 in the Navajo Nation and surrounding states (Arizona, Colorado, New Mexico, and Utah)
During an early period of the Coronavirus Disease 2019 (COVID-19) pandemic, the Navajo Nation, much like New York City, experienced a relatively high rate of disease transmission. Yet, between January and October 2020, it experienced only a single period of growth in new COVID-19 cases, which ended when cases peaked in May 2020. The daily number of new cases slowly decayed in the summer of 2020 until late September 2020. In contrast, the surrounding states of Arizona, Colorado, New Mexico, and Utah all experienced at least two periods of growth in the same time frame, with second surges beginning in late May to early June. Here, we investigated these differences in disease transmission dynamics with the objective of quantifying the contributions of non-pharmaceutical interventions (NPIs) (e.g., behaviors that limit disease transmission). We considered a compartmental model accounting for distinct periods of NPIs to analyze the epidemic in each of the five regions. We used Bayesian inference to estimate region-specific model parameters from regional surveillance data (daily reports of new COVID-19 cases) and to quantify uncertainty in parameter estimates and model predictions. Our results suggest that NPIs in the Navajo Nation were sustained over the period of interest, whereas in the surrounding states, NPIs were relaxed, which allowed for subsequent surges in cases. Our region-specific model parameterizations allow us to quantify the impacts of NPIs on disease incidence in the regions of interest.
60 APPLIED LIFE SCIENCES↗
A Mathematical Model of Breast Tumor Progression Based on Immune Infiltration
Breast cancer is the most prominent type of cancer among women. Understanding the microenvironment of breast cancer and the interactions between cells and cytokines will lead to better treatment approaches for patients. In this study, we developed a data-driven mathematical model to investigate the dynamics of key cells and cytokines involved in breast cancer development. We used gene expression profiles of tumors to estimate the relative abundance of each immune cell and group patients based on their immune patterns. Dynamical results show the complex interplay between cells and molecules, and sensitivity analysis emphasizes the direct effects of macrophages and adipocytes on cancer cell growth. In addition, we observed the dual effect of IFN-γ on cancer proliferation, either through direct inhibition of cancer cells or by increasing the cytotoxicity of CD8+ T-cells.
60 APPLIED LIFE SCIENCES↗
Stochastic Error Cancellation in Analog Quantum Simulation
Analog quantum simulation is a promising path towards solving classically intractable problems in many-body physics on near-term quantum devices. However, the presence of noise limits the size of the system and the length of time that can be simulated. In our work, we consider an error model in which the actual Hamiltonian of the simulator differs from the target Hamiltonian we want to simulate by small local perturbations, which are assumed to be random and unbiased. We analyze the error accumulated in observables in this setting and show that, due to stochastic error cancellation, with high probability the error scales as the square root of the number of qubits instead of linearly. We explore the concentration phenomenon of this error as well as its implications for local observables in the thermodynamic limit. Moreover, we show that stochastic error cancellation also manifests in the fidelity between the target state at the end of time-evolution and the actual state we obtain in the presence of noise. This indicates that, to reach a certain fidelity, more noise can be tolerated than implied by the worst-case bound if the noise comes from many statistically independent sources.
Analog quantum simulation↗
INVERSION PROPERTY OF THE FUNDAMENTAL MATRIX IN TRAJECTORY PERTURBATION PROBLEMS
Inversion of ordinary linear differential equation in matrix form for application to class of trajectory perturbation problems
LINEAR EQUATION↗
Study of non-linear optimization techniques
Nonlinear optimization techniques in dynamic programming and solution of ordinary nonlinear differential equations by Runge-Kutta method
Shanks, E. B.↗
Transient-forced convection film boiling on an isothermal flat plate
An approach for the solution of transient-forced convection film boiling on an isothermal flat plate using the boundary layer model is developed. The similarity variables are used to convert the governing partial differential equations to ordinary ones. The results of numerical solutions of these ordinary equations indicate that the transient process can be classified as one-dimensional conduction, intermediate, and the steady state regions. The time required for the one-dimensional conduction and the time necessary to attain a steady state condition are obtained. The influence of interfacial shear is seen to be negligible while the Prandtl Number and the ratio (C sub p delta T divided by h sub fg times Pr) have major influence. The use of local similarity approximations for the intermediate regime facilitates prediction of complete boundary layer growth. Using the ratio of time at any instant to the steady state time as abscissa, the curves representing the boundary layer growth can be merged into a single mean curve within 5 percent. Further, the analysis shows that the average rate of heat transfer during transient is 50 to 100 percent higher than those at steady state. The average rate of vapor convected away is 10 to 15 percent lower than at steady state while the average rate of accumulation to form the vapor layer is 1 to 14 times larger. Further, the total heat transferred during transient increases and the evaporation decreases for increasing values of C sub p delta T divided by h sub fg times Pr
Nagendra, H. R.↗
Transient-forced convection film boiling on an isothermal flat plate.
Development of a new approach for the solution of transient-forced convection film boiling on an isothermal flat plate using the boundary layer model. The similarity variables are used to convert the governing partial differential equations to ordinary ones. The results of numerical solutions of these ordinary equations indicate that the transient process can be classified as one-dimensional conduction, intermediate, and the steady-state regions. The time required for the one-dimensional conduction and the time necessary to attain a steady-state condition are obtained. The use of local similarity approximations for the intermediate regime facilitates prediction of complete boundary layer growth. Using the ratio of time at any instant to the steady-state time as abscissa, the curves representing the boundary layer growth can be merged into a single mean curve within 5%. Further, the analysis shows that the average rate of heat transfer during transient is 50 to 100% higher than those at steady state. The average rate of vapor convected away is 10 to 15% lower than at steady state while the average rate of accumulation to form the vapor layer is 1 to 14 times larger.
Nagendra, H. R.↗
On the stability of numerical integration routines.
Numerical integration methods for the solution of initial value problems for ordinary vector differential equations may be modelled as discrete time feedback systems. The stability criteria discovered in modern control theory are applied to these systems and criteria involving the routine, the step size and the differential equation are derived. Linear multistep, Runge-Kutta, and predictor-corrector methods are all investigated.
Glover, K.↗
Automating the parallel processing of fluid and structural dynamics calculations
The NASA Lewis Research Center is actively involved in the development of expert system technology to assist users in applying parallel processing to computational fluid and structural dynamic analysis. The goal of this effort is to eliminate the necessity for the physical scientist to become a computer scientist in order to effectively use the computer as a research tool. Programming and operating software utilities have previously been developed to solve systems of ordinary nonlinear differential equations on parallel scalar processors. Current efforts are aimed at extending these capabilities to systems of partial differential equations, that describe the complex behavior of fluids and structures within aerospace propulsion systems. This paper presents some important considerations in the redesign, in particular, the need for algorithms and software utilities that can automatically identify data flow patterns in the application program and partition and allocate calculations to the parallel processors. A library-oriented multiprocessing concept for integrating the hardware and software functions is described.
Arpasi, Dale J.↗
The nonlinear interaction of Tollmien-Schlichting waves and Taylor-Goertler vortices in curved channel flows
It is known that a viscous fluid flow with curved streamlines can support both Tollmien-Schlichting and Taylor-Goertler instabilities. In a situation where both modes are possible on the basis of linear theory a nonlinear theory must be used to determine the effect of the interaction of the instabilities. The details of this interaction are of practical importance because of its possible catastrophic effects on mechanisms used for laminar flow control. This interaction is studied in the context of fully developed flows in curved channels. A part form technical differences associated with boundary layer growth the structures of the instabilities in this flow are very similar to those in the practically more important external boundary layer situation. The interaction is shown to have two distinct phases depending on the size of the disturbances. At very low amplitudes two oblique Tollmein-Schlichting waves interact with a Goertler vortex in such a manner that the amplitudes become infinite at a finite time. This type of interaction is described by ordinary differential amplitude equations with quadratic nonlinearities.
Hall, P.↗
Automating the parallel processing of fluid and structural dynamics calculations
The NASA Lewis Research Center is actively involved in the development of expert system technology to assist users in applying parallel processing to computational fluid and structural dynamic analysis. The goal of this effort is to eliminate the necessity for the physical scientist to become a computer scientist in order to effectively use the computer as a research tool. Programming and operating software utilities have previously been developed to solve systems of ordinary nonlinear differential equations on parallel scalar processors. Current efforts are aimed at extending these capabilties to systems of partial differential equations, that describe the complex behavior of fluids and structures within aerospace propulsion systems. This paper presents some important considerations in the redesign, in particular, the need for algorithms and software utilities that can automatically identify data flow patterns in the application program and partition and allocate calculations to the parallel processors. A library-oriented multiprocessing concept for integrating the hardware and software functions is described.
Arpasi, Dale J.↗
Computational methods for probability of instability calculations
This paper summarizes the development of the methods and a computer program to compute the probability of instability of a dynamic system than can be represented by a system of second-order ordinary linear differential equations. Two instability criteria based upon the roots of the characteristics equation or Routh-Hurwitz test functions are investigated. Computational methods based on system reliability analysis methods and importance sampling concepts are proposed to perform efficient probabilistic analysis. Numerical examples are provided to demonstrate the methods.
Wu, Y.-T.↗
Probabilistic methods for rotordynamics analysis
This paper summarizes the development of the methods and a computer program to compute the probability of instability of dynamic systems that can be represented by a system of second-order ordinary linear differential equations. Two instability criteria based upon the eigenvalues or Routh-Hurwitz test functions are investigated. Computational methods based on a fast probability integration concept and an efficient adaptive importance sampling method are proposed to perform efficient probabilistic analysis. A numerical example is provided to demonstrate the methods.
Wu, Y.-T.↗