Neural Network Methods for Boundary Value Problems Defined in Arbitrarily Shaped Domains

Partial differential equations (PDEs) with Dirichlet boundary conditions defined on boundaries with simple geometry have been succesfuly treated using sigmoidal multilayer perceptrons in previous works. This article deals with the case of complex boundary geometry, where the boundary is determined by a number of points that belong to it and are closely located, so as to offer a reasonable representation. Two networks are employed: a multilayer perceptron and a radial basis function network. The later is used to account for the satisfaction of the boundary conditions. The method has been successfuly tested on two-dimensional and three-dimensional PDEs and has yielded accurate solutions

Artificial Neural Networks for Solving Ordinary and Partial Differential Equations

We present a method to solve initial and boundary value problems using artificial neural networks. A trial solution of the differential equation is written as a sum of two parts. The first part satisfies the boundary (or initial) conditions and contains no adjustable parameters. The second part is constructed so as not to affect the boundary conditions. This part involves a feedforward neural network, containing adjustable parameters (the weights). Hence by construction the boundary conditions are satisfied and the network is trained to satisfy the differential equation. The applicability of this approach ranges from single ODE's, to systems of coupled ODE's and also to PDE's. In this article we illustrate the method by solving a variety of model problems and present comparisons with finite elements for several cases of partial differential equations.Comment: LAtex file, 26 pages, 21 figs, submitted to IEEE TN

Quadratic momentum dependence in the nucleon-nucleon interaction

We investigate different choices for the quadratic momentum dependence required in nucleon-nucleon potentials to fit phase shifts in high partial-waves. In the Argonne v18 potential L**2 and (L.S)**2 operators are used to represent this dependence. The v18 potential is simple to use in many-body calculations since it has no quadratic momentum-dependent terms in S-waves. However, p**2 rather than L**2 dependence occurs naturally in meson-exchange models of nuclear forces. We construct an alternate version of the Argonne potential, designated Argonne v18pq, in which the L**2 and (L.S)**2 operators are replaced by p**2 and Qij operators, respectively. The quadratic momentum-dependent terms are smaller in the v18pq than in the v18 interaction. Results for the ground state binding energies of 3H, 3He, and 4He, obtained with the variational Monte Carlo method, are presented for both the models with and without three-nucleon interactions. We find that the nuclear wave functions obtained with the v18pq are slightly larger than those with v18 at interparticle distances < 1 fm. The two models provide essentially the same binding in the light nuclei, although the v18pq gains less attraction when a fixed three-nucleon potential is added.Comment: v.2 important corrections in tables and minor revisions in text; reference for web-posted subroutine adde

Short-range Correlations in a CBF description of closed-shell nuclei

The Correlated Basis Function theory (CBF) provides a theoretical framework to treat on the same ground mean-field and short-range correlations. We present, in this report, some recent results obtained using the CBF to describe the ground state properties of finite nuclear systems. Furthermore we show some results for the excited state obtained with a simplified model based on the CBF theory.Comment: 10 latex pages plus 6 uuencoded figure

Fast Neural Network Predictions from Constrained Aerodynamics Datasets

Incorporating computational fluid dynamics in the design process of jets, spacecraft, or gas turbine engines is often challenged by the required computational resources and simulation time, which depend on the chosen physics-based computational models and grid resolutions. An ongoing problem in the field is how to simulate these systems faster but with sufficient accuracy. While many approaches involve simplified models of the underlying physics, others are model-free and make predictions based only on existing simulation data. We present a novel model-free approach in which we reformulate the simulation problem to effectively increase the size of constrained pre-computed datasets and introduce a novel neural network architecture (called a cluster network) with an inductive bias well-suited to highly nonlinear computational fluid dynamics solutions. Compared to the state-of-the-art in model-based approximations, we show that our approach is nearly as accurate, an order of magnitude faster, and easier to apply. Furthermore, we show that our method outperforms other model-free approaches

Adaptive Memetic Particle Swarm Optimization with Variable Local Search Pool Size

We propose an adaptive Memetic Particle Swarm Optimization algorithm where local search is selected from a pool of different algorithms. The choice of local search is based on a probabilistic strategy that uses a simple metric to score the efficiency of local search. Our study investigates whether the pool size affects the memetic algorithm’s performance, as well as the possible benefit of using the adaptive strategy against a baseline static one. For this purpose, we employed the memetic algorithms framework provided in the recent MEMPSODE optimization software, and tested the proposed algorithms on the Benchmarking Black Box Optimization (BBOB 2012) test suite. The obtained results lead to a series of useful conclusions

Spin-Isospin Structure and Pion Condensation in Nucleon Matter

We report variational calculations of symmetric nuclear matter and pure neutron matter, using the new Argonne v18 two-nucleon and Urbana IX three-nucleon interactions. At the equilibrium density of 0.16 fm^-3 the two-nucleon densities in symmetric nuclear matter are found to exhibit a short-range spin-isospin structure similar to that found in light nuclei. We also find that both symmetric nuclear matter and pure neutron matter undergo transitions to phases with pion condensation at densities of 0.32 fm^-3 and 0.2 fm^-3, respectively. Neither transtion occurs with the Urbana v14 two-nucleon interaction, while only the transition in neutron matter occurs with the Argonne v14 two-nucleon interaction. The three-nucleon interaction is required for the transition to occur in symmetric nuclear matter, whereas the the transition in pure neutron matter occurs even in its absence. The behavior of the isovector spin-longitudinal response and the pion excess in the vicinity of the transition, and the model dependence of the transition are discussed.Comment: 44 pages RevTeX, 15 postscript figures. Minor modifications to original postin

EU support for biofuels and bioenergy, environmental sustainability criteria, and trade policy

This paper presents physics-based surrogate modeling algorithms for systems governed by parameterized partial differential equations (PDEs) commonly encountered in design optimization and uncertainty analysis. We first outline unsupervised learning approaches that leverage advances in the machine learning literature for a meshfree solution of PDEs. Subsequently, we propose continuum and discrete formulations for systems governed by parameterized steady-state PDEs. We consider the case of both deterministically and randomly parameterized systems. The basic idea is to embody the design variables or uncertain parameters in additional dimensions of the governing PDEs along with the spatial coordinates. We show that the undetermined parameters of the surrogate model can be estimated by minimizing a physics-based objective function derived using a multidimensional least-squares collocation or the Bubnov-Galerkin scheme. This potentially allows us to construct surrogate models without using data from computer experiments on a deterministic analysis code. Finally, we also outline an extension of the present approach to directly approximate the density function of random algebraic equations

Ground state of N=Z doubly closed shell nuclei in CBF theory

The ground state properties of N=Z doubly closed shell nuclei are studied within correlated basis function theory. A truncated version of the Urbana v14 realistic potential, with spin, isospin and tensor components, is adopted, together with state dependent correlations. Fermi hypernetted chain integral equation and single operator chain approximation are used to evaluate density, distribution function and ground state energy of 16O and 40Ca. The results favourably compare with the available, variational MonteCarlo estimates and provide a first substantial check of the accuracy of the cluster summation method for state dependent correlations. We achieve in finite nuclei at least the same level of accuracy in the treatment of non central interactions and correlations as in nuclear matter. This opens the way for a microscopic study of medium heavy nuclei ground state using present days realistic hamiltonians.Comment: 35 pages (LateX) + 3 figures. Phys.Rev.C, in pres