Zobrazeno 1 - 10
of 2 796
pro vyhledávání: '"KUMAR, KRISHNA"'
Autor:
Gal, Ciprian, Ghosh, Chandan, Park, Sanghwa, Adhikari, Devi, Armstrong, David, Beminiwattha, Rakitha, Camsonne, Alexandre, Chandrasena, Shashini, Dalton, Mark, Deshpande, Abhay, Gaskell, Dave, Higinbotham, Douglas, Horowitz, Charles J., King, Paul, Kumar, Krishna, Kutz, Tyler, Mammei, Juliette, McNulty, Dustin, Michaels, Robert, Palatchi, Caryn, Panta, Anil, Paschke, Kent, Pitt, Mark, Sen, Arindam, Simicevic, Neven, Weliyanga, Lasitha, Wells, Steven P.
We propose to measure the beam normal single spin asymmetry in elastic scattering of transversely polarized electron from target nuclei with 12 $\leq Z \leq$ 90 at Q$^2$ = 0.0092 GeV$^2$ to study its nuclear dependence. While the theoretical calculat
Externí odkaz:
http://arxiv.org/abs/2411.10267
Artificial intelligence (AI) has become a buzz word since Google's AlphaGo beat a world champion in 2017. In the past five years, machine learning as a subset of the broader category of AI has obtained considerable attention in the research community
Externí odkaz:
http://arxiv.org/abs/2410.14767
We present Basis-to-Basis (B2B) operator learning, a novel approach for learning operators on Hilbert spaces of functions based on the foundational ideas of function encoders. We decompose the task of learning operators into two parts: learning sets
Externí odkaz:
http://arxiv.org/abs/2410.00171
Publikováno v:
Computers and Geotechnics (2024)
Numerical modeling of slope failures seeks to predict two key phenomena: the initiation of failure and the post-failure runout. Currently, most modeling methods for slope failure analysis excel at one of these two but are deficient in the other. For
Externí odkaz:
http://arxiv.org/abs/2407.05185
Earthquake-induced liquefaction can cause substantial lateral spreading, posing threats to infrastructure. Machine learning (ML) can improve lateral spreading prediction models by capturing complex soil characteristics and site conditions. However, t
Externí odkaz:
http://arxiv.org/abs/2404.15959
Tailings dams impound large amounts of saturated soil which can be highly susceptible to liquefaction. Liquefaction results in a severe loss of strength in the retained soil and potentially failure of the dam. If the dam is breached, a massive debris
Externí odkaz:
http://arxiv.org/abs/2404.15860
The collision avoidance constraints are prominent as non-convex, non-differentiable, and challenging when defined in optimization-based motion planning problems. To overcome these issues, this paper presents a novel non-conservative collision avoidan
Externí odkaz:
http://arxiv.org/abs/2404.07916
Autor:
Choi, Yongjin, Kumar, Krishna
Inverse problems in granular flows, such as landslides and debris flows, involve estimating material parameters or boundary conditions based on target runout profile. Traditional high-fidelity simulators for these inverse problems are computationally
Externí odkaz:
http://arxiv.org/abs/2401.13695
Autor:
Vajapeyajula, Anusha, Kumar, Krishna
Underground duct banks carrying power cables dissipate heat to the surrounding soil. The amount of heat dissipated determines the current rating of cables, which in turn affects the sizing of the cables. The dissipation of heat through the surroundin
Externí odkaz:
http://arxiv.org/abs/2312.15293
Non-contrastive self-supervised learning (NC-SSL) methods like BarlowTwins and VICReg have shown great promise for label-free representation learning in computer vision. Despite the apparent simplicity of these techniques, researchers must rely on se
Externí odkaz:
http://arxiv.org/abs/2312.10725