output
20122022
most citedLogarithmic temperature profiles in turbulent Rayleigh-Bénard convection

104 citations

9 papers

cs.LG2022★ 70 cited

Modular machine learning-based elastoplasticity: generalization in the context of limited data

Jan N. Fuhg, Craig M. Hamel, Kyle Johnson +2

The development of accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics. Challenges…

cond-mat.soft2022★ 43 cited

A statistical mechanics framework for polymer chain scission, based on the concepts of distorted bond potential and asymptotic matching

Jason Mulderrig, Brandon Talamini, Nikolaos Bouklas

To design increasingly tough, resilient, and fatigue-resistant elastomers and hydrogels, the relationship between controllable network parameters at the molecular level to macrosco…

nlin.PS2022★ 2 cited

Synchronization in pairs of opto-thermally driven mechanically coupled micro-oscillators

Aditya Bhaskar, Mark Walth, Richard H. Rand +1

We study the phenomenon of synchronization in pairs of doubly clamped, mechanically coupled silicon micro-oscillators. A continuous-wave laser beam is used to drive the micro-beams…

eess.SY2022★ 3 cited

Elliptical Slice Sampling for Probabilistic Verification of Stochastic Systems with Signal Temporal Logic Specifications

Guy Scher, Sadra Sadraddini, Russ Tedrake +1

Autonomous robots typically incorporate complex sensors in their decision-making and control loops. These sensors, such as cameras and Lidars, have imperfections in their sensing a…

nlin.PS2021★ 1 cited

Bistability in coupled opto-thermal micro-oscillators

Aditya Bhaskar, Mark Walth, Richard H. Rand +1

In this work, we experimentally investigate the dynamics of pairs of opto-thermally driven, mechanically coupled, doubly clamped, silicon micromechanical oscillators, and numerical…

cs.CE2021★ 24 cited

Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties

T. Kadeethum, D. O'Malley, Y. Choi +3

Machine learning-based data-driven modeling can allow computationally efficient time-dependent solutions of PDEs, such as those that describe subsurface multiphysical problems. In…

cs.CE2021★ 67 cited

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques

T. Kadeethum, F. Ballarin, Y. Choi +3

Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g., the process of sequestration). Here,…

cs.CE2020★ 33 cited

A Locally Conservative Mixed Finite Element Framework for Coupled Hydro-Mechanical-Chemical Processes in Heterogeneous Porous Media

T. Kadeethum, S. Lee, F. Ballarin +2

This paper presents a mixed finite element framework for coupled hydro-mechanical-chemical processes in heterogeneous porous media. The framework combines two types of locally cons…

physics.flu-dyn2012★ 104 cited

Logarithmic temperature profiles in turbulent Rayleigh-Bénard convection

Guenter Ahlers, Eberhard Bodenschatz, Denis Funfschilling +5

We report results for the temperature profiles of turbulent Rayleigh-Bénard convection (RBC) in the interior of a cylindrical sample of aspect ratio ( and $…