Zobrazeno 1 - 10
of 14 672
pro vyhledávání: '"AKIL, A"'
Autor:
Lowery, Matthew, Turnage, John, Morrow, Zachary, Jakeman, John D., Narayan, Akil, Zhe, Shandian, Shankar, Varun
This paper introduces the Kernel Neural Operator (KNO), a novel operator learning technique that uses deep kernel-based integral operators in conjunction with quadrature for function-space approximation of operators (maps from functions to functions)
Externí odkaz:
http://arxiv.org/abs/2407.00809
Autor:
Cepollaro, Carlo, Akil, Ali, Cieśliński, Paweł, de la Hamette, Anne-Catherine, Brukner, Časlav
Recent work on quantum reference frames (QRFs) has demonstrated that superposition and entanglement are properties that change under QRF transformations. Given their utility in quantum information processing, it is important to understand how a mere
Externí odkaz:
http://arxiv.org/abs/2406.19448
Ellipse estimation is an important topic in food image processing because it can be leveraged to parameterize plates and bowls, which in turn can be used to estimate camera view angles and food portion sizes. Automatically detecting the elliptical ri
Externí odkaz:
http://arxiv.org/abs/2405.07121
Autor:
Marani, Amin Hosseiny, Schnaithmann, Ulie, Son, Youngseo, Iyer, Akil, Paldhe, Manas, Raghuvanshi, Arushi
Current Conversational AI systems employ different machine learning pipelines, as well as external knowledge sources and business logic to predict the next action. Maintaining various components in dialogue managers' pipeline adds complexity in expan
Externí odkaz:
http://arxiv.org/abs/2404.08155
We introduce the Transformed Generative Pre-Trained Physics-Informed Neural Networks (TGPT-PINN) for accomplishing nonlinear model order reduction (MOR) of transport-dominated partial differential equations in an MOR-integrating PINNs framework. Buil
Externí odkaz:
http://arxiv.org/abs/2403.03459
We present a novel and comparative analysis of finite element discretizations for a nonlinear Rosenau-Burgers model including a biharmonic term. We analyze both continuous and mixed finite element approaches, providing stability, existence, and uniqu
Externí odkaz:
http://arxiv.org/abs/2402.08926
Autor:
Berggren, Caleb C., Jiang, David, Wang, Y. F. Jack, Bergquist, Jake A., Rupp, Lindsay C., Liu, Zexin, MacLeod, Rob S., Narayan, Akil, Timmins, Lucas H.
Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany
Externí odkaz:
http://arxiv.org/abs/2401.15047
The shallow water flow model is widely used to describe water flows in rivers, lakes, and coastal areas. Accounting for uncertainty in the corresponding transport-dominated nonlinear PDE models presents theoretical and numerical challenges that motiv
Externí odkaz:
http://arxiv.org/abs/2310.06229
Fourier Neural Operator (FNO) is a popular operator learning framework. It not only achieves the state-of-the-art performance in many tasks, but also is efficient in training and prediction. However, collecting training data for the FNO can be a cost
Externí odkaz:
http://arxiv.org/abs/2309.16971
In this paper we study the stability of two different problems. The first one is a one-dimensional degenerate wave equation with degenerate damping, incorporating a drift term and a leading operator in non-divergence form. In the second problem we co
Externí odkaz:
http://arxiv.org/abs/2308.08645