Results 11 to 20 of about 985,022 (264)
Tecnura, 2012 This paper describes the design, modeling and implementation of a single-phase inverter connected to the grid from renewable energy sources. We study the model in small signal to be below the control loops design it.
César Leonardo Trujillo Rodríguez +4 more
doaj +2 more sources
Diffusion Posterior Sampling for General Noisy Inverse Problems [PDF]
International Conference on Learning Representations, 2022 Diffusion models have been recently studied as powerful generative inverse problem solvers, owing to their high quality reconstructions and the ease of combining existing iterative solvers.
Hyungjin Chung +4 more
semanticscholar +1 more source
INVERSOR OF DIGITS OF TWO-BASE G–REPRESENTATION OF REAL NUMBERS AND ITS STRUCTURAL FRACTALITY
Bukovinian Mathematical Journal, 2022 In the paper, we introduce a new two-symbol system of representation for numbers from segment $[0;0,5]$ with alphabet (set of digits) $A=\{0;1\}$ and two bases 2 and $-2$: \[x=\dfrac{\alpha_1}{2}+\dfrac{1}{2}\sum\limits^\infty_{k=1}\dfrac{\alpha_{k+1}}{2^
M. Pratsiovytyi +3 more
semanticscholar +1 more source
Improving Diffusion Models for Inverse Problems using Manifold Constraints [PDF]
Neural Information Processing Systems, 2022 Recently, diffusion models have been used to solve various inverse problems in an unsupervised manner with appropriate modifications to the sampling process.
Hyungjin Chung +3 more
semanticscholar +1 more source
A Variational Perspective on Solving Inverse Problems with Diffusion Models [PDF]
International Conference on Learning Representations, 2023 Diffusion models have emerged as a key pillar of foundation models in visual domains. One of their critical applications is to universally solve different downstream inverse tasks via a single diffusion prior without re-training for each task.
M. Mardani +3 more
semanticscholar +1 more source
Physics-informed neural networks (PINNs) for fluid mechanics: a review [PDF]
Acta Mechanica Sinica, 2021 Despite the significant progress over the last 50 years in simulating flow problems using numerical discretization of the Navier–Stokes equations (NSE), we still cannot incorporate seamlessly noisy data into existing algorithms, mesh-generation is ...
Shengze Cai +4 more
semanticscholar +1 more source
GS-IR: 3D Gaussian Splatting for Inverse Rendering [PDF]
Computer Vision and Pattern Recognition, 2023 We propose GS-IR, a novel inverse rendering approach based on 3D Gaussian Splatting (3DGS) that leverages forward mapping volume rendering to achieve photorealistic novel view synthesis and relighting results.
Zhihao Liang +4 more
semanticscholar +1 more source
Physics-informed neural networks with hard constraints for inverse design [PDF]
SIAM Journal on Scientific Computing, 2021 Inverse design arises in a variety of areas in engineering such as acoustic, mechanics, thermal/electronic transport, electromagnetism, and optics. Topology optimization is a major form of inverse design, where we optimize a designed geometry to achieve ...
Lu Lu +5 more
semanticscholar +1 more source
TecnoLógicas, 2020 La generación de electricidad amigable con el medio ambiente es un factor fundamental para el crecimiento económico y social de cualquier país. Recientemente la instalación de sistemas de generación fotovoltaicos se ha incrementado a nivel local, aunque ...
Claudia Lucía Cortés Cortés +4 more
semanticscholar +1 more source
Inverse Scaling: When Bigger Isn't Better [PDF]
Trans. Mach. Learn. Res., 2023 Work on scaling laws has found that large language models (LMs) show predictable improvements to overall loss with increased scale (model size, training data, and compute).
I. R. McKenzie +26 more
semanticscholar +1 more source