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Stinis, Panos

Publications and source records attributed to Stinis, Panos.

25 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Data for publication Wan et al. (2020, JAMES), Improving time-step convergence in an atmosphere model with simplified physics: the impacts of closure assumption and process coupling

This dataset contains simple condensation mdoel output and CAM4 model output that were used to produce figures in the following paper: Wan, H., Woodward, C. S., Zhang, S., Vogl, C. J., Stinis, P., Gardner, D. J., Rasch, P. J., Zeng, X., Larson, V. E., and Singh, B. (2020): Improving time-step convergence in an atmosphere model with simplified physics: the impacts of closure assumption and process coupling, Journal of Advances in Modeling Earth Systems, https://doi.org/10.1029/2019MS001982

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Data from Wan et al. (2020, JAMES, DOI: 10.1029/2019MS001982)

This data set includes the simple condensation model output and CAM4 model output used to generate figures presented in the following paper: Wan, H., Woodward, C. S., Zhang, S., Vogl, C. J., Stinis, P., Gardner, D. J., Rasch, P. J., Zeng, X., Larson, V. E., and Singh, B. (2020): Improving time-step convergence in an atmosphere model with simplified physics: the impacts of closure assumption and process coupling, Journal of Advances in Modeling Earth Systems, https://doi.org/10.1029/2019MS001982

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks

Generative Adversarial Networks (GANs) are becoming popular choices for unsupervised learning. At the same time there is a concerted effort in the machine learning community to expand the range of tasks in which learning can be applied as well as to utilize methods from other disciplines to accelerate learning. With this in mind, in the current work we suggest ways to enforce given constraints in the output of a GAN both for interpolation and extrapolation. The two cases need to be treated differently. For the case of interpolation, the incorporation of constraints is built into the training of the GAN. The incorporation of the constraints respects the primary game-theoretic setup of a GAN so it can be combined with existing algorithms. However, it can exacerbate the problem of instability during training that is well-known for GANs. We suggest adding small noise to the constraints as a simple remedy that has performed well in our numerical experiments. The case of extrapolation (prediction) is more involved. First, we employ a modified interpolation training process that uses noisy data but does {\it not} necessarily enforce the constraints during training. Second, the resulting modified interpolator is used for extrapolation where the constraints are enforced after each step through projection on the space of constraints.

Stinis, Panagiotis↗
DOE OSTI2018

Dynamic Looping of a Free-Draining Polymer

Here, we revisit the celebrated Wilemski--Fixman (WF) treatment for the looping time of a free-draining polymer. The WF theory introduces a sink term into the Fokker--Planck equation for the $3(N+1)$-dimensional Ornstein--Uhlenbeck process of polymer dynamics, which accounts for the appropriate boundary condition due to the formation of a loop. The assumption for WF theory is considerably relaxed. A perturbation method approach is developed that justifies and generalizes the previous results using either a delta sink or a Heaviside sink. For both types of sinks, we show that under the condition of a small dimensionless $\epsilon$, the ratio of capture radius to the Kuhn length, we are able to systematically produce all known analytical and asymptotic results obtained by other methods. This includes most notably the transition regime between the $N^2$ scaling of Doi, and $N\sqrt{N}/\epsilon$ scaling of Szabo, Schulten, and Schulten. The mathematical issue at play is the nonuniform convergence of $\epsilon\to 0$ and $N\to\infty$, the latter being an inherent part of the theory of a Gaussian polymer. Our analysis yields a novel term in the analytical expression for the looping time with small $\epsilon$, which was previously unknown. Monte Carlo numerical simulations corroborate the analytical findings. The systematic method developed here can be applied to other systems modeled by multidimensional Smoluchowski equations.

Ye, Felix X. -F.↗
DOE OSTI2016

Final Report

This is the final report for the work conducted at the University of Minnesota (during the period 12/01/12-09/18/14) by PI Panos Stinis as part of the "Collaboratory on Mathematics for Mesoscopic Modeling of Materials" (CM4). CM4 is a multi-institution DOE-funded project whose aim is to conduct basic and applied research in the emerging field of mesoscopic modeling of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Mesh refinement for uncertainty quantification through model reduction

We present a novel way of deciding when and where to refine a mesh in probability space in order to facilitate uncertainty quantification in the presence of discontinuities in random space. A discontinuity in random space makes the application of generalized polynomial chaos expansion techniques prohibitively expensive. The reason is that for discontinuous problems, the expansion converges very slowly. An alternative to using higher terms in the expansion is to divide the random space in smaller elements where a lower degree polynomial is adequate to describe the randomness. In general, the partition of the random space is a dynamic process since some areas of the random space, particularly around the discontinuity, need more refinement than others as time evolves. In the current work we propose a way to decide when and where to refine the random space mesh based on the use of a reduced model. The idea is that a good reduced model can monitor accurately, within a random space element, the cascade of activity to higher degree terms in the chaos expansion. In turn, this facilitates the efficient allocation of computational sources to the areas of random space where they are more needed. For the Kraichnan–Orszag system, the prototypical system to study discontinuities in random space, we present theoretical results which show why the proposed method is sound and numerical results which corroborate the theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗