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pro vyhledávání: '"Smith Jonathan D"'
Increased demand for high-performance permanent magnets in the electric vehicle and wind turbine industries has prompted the search for cost-effective alternatives.Discovering new magnetic materials with the desired intrinsic and extrinsic permanent
Externí odkaz:
http://arxiv.org/abs/2312.02475
Each point of a simplex is expressed as a unique convex combination of the vertices. The coefficients in the combination are the barycentric coordinates of the point. For each point in a general convex polytope, there may be multiple representations,
Externí odkaz:
http://arxiv.org/abs/2312.00828
The associative Cayley-Dickson algebras over the field of real numbers are also Clifford algebras. The alternative but nonassociative real Cayley-Dickson algebras, notably the octonions and split octonions, share with Clifford algebras an involutary
Externí odkaz:
http://arxiv.org/abs/2310.09972
Publikováno v:
Gavin N Nop et al 2024 Quantum Sci. Technol. 9 035015
Junctions are fundamental elements that support qubit locomotion in two-dimensional ion trap arrays and enhance connectivity in emerging trapped-ion quantum computers. In surface ion traps they have typically been implemented by shaping radio frequen
Externí odkaz:
http://arxiv.org/abs/2310.07195
Autor:
Smith, Jonathan D., Hall, Samuel, Coombs, George, Byrne, James, Thorne, Michael A. S., Brearley, J. Alexander, Long, Derek, Meredith, Michael, Fox, Maria
We introduce a method for long-distance maritime route planning in polar regions, taking into account complex changing environmental conditions. The method allows the construction of optimised routes, describing the three main stages of the process:
Externí odkaz:
http://arxiv.org/abs/2209.02389
We introduce a scheme for probabilistic hypocenter inversion with Stein variational inference. Our approach uses a differentiable forward model in the form of a physics informed neural network, which we train to solve the Eikonal equation. This allow
Externí odkaz:
http://arxiv.org/abs/2101.03271
The recent deep learning revolution has created an enormous opportunity for accelerating compute capabilities in the context of physics-based simulations. Here, we propose EikoNet, a deep learning approach to solving the Eikonal equation, which chara
Externí odkaz:
http://arxiv.org/abs/2004.00361
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