Optimal scheduling of hydrogen production in dynamic pricing power market can maximize the profit of hydrogen producer; however, it highly depends on the accurate forecast of hydrogen consumption. In this paper, we propose a deep leaning based forecasting approach for predicting hydrogen consumption of fuel cell vehicl…
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28 results for “hydrogen”
China and EU race to develop hydrogen for energy transition.
problem Developing hydrogen for sustainable energy systems.
method Comparative analysis framework using key factors.
result Customized solutions for local hydrogen industries.
Low redispatch prices boost green hydrogen production cost, encouraging electrolyzer siting.
problem Uncertainty in redispatch power availability and its impact on green hydrogen production cost.
method Historic redispatch time series analysis and power purchase scenarios evaluation.
result Low price levels can lead to notable production cost reductions, incentivizing electrolyzer siting.
Hydrogen atom confined inside an inverted-Gaussian potentialphysics.atom-ph
Hydrogen atom confined in an inverted-Gaussian potential, with detailed numerical methods and results.
problem Studying hydrogen atom in a specific potential.
method Three numerical methods: Lagrange-mesh, fourth order finite differences, and finite element method.
result Accurate numerical results for hydrogen atom energies and eigenfunctions, improving previous literature.
AI speeds up hydrogen fuel cell stack development time.
problem Reducing time to market for hydrogen fuel cell stacks.
method Generative Adversarial Networks for data augmentation.
result Reduced testing time from 1000 hours to 3 hours.
Mathematician summarizes protein geometry and mutation effects.
problem Understanding how proteins mutate and their structure-function relationship.
method Mathematical analysis of protein structures and functions, focusing on hydrogen bonds and secondary structure.
result Protein secondary structure regulates mutation by stabilizing or destabilizing regions.
We extend classical Euclidean stability theorems corresponding to the nonrelativistic Hamiltonians of ions with one electron to the setting of non parabolic Riemannian 3-manifolds.
Fully probabilistic quasar continua predictions near Lyman-α with conditional neural spline flowsastro-ph.CO
New method predicts quasar continuum near Lyman-α with high precision and accuracy.
problem Precise measurement of quasar red damping wing for epoch of reionization.
method Fully probabilistic approach using conditional neural spline flows.
result Achieved state-of-the-art precision and accuracy in predicting quasar continua.
[New and updated results were published in Nature Chemistry, doi:10.1038/s41557-020-0544-y.] The electronic Schrödinger equation describes fundamental properties of molecules and materials, but can only be solved analytically for the hydrogen atom. The numerically exact full configuration-interaction method is exponent…
Unsupervised Temporal Clustering to Monitor the Performance of Alternative Fueling Infrastructurecs.CY
Zero Emission Vehicles (ZEV) play an important role in the decarbonization of the transportation sector. For a wider adoption of ZEVs, providing a reliable infrastructure is critical. We present a machine learning approach that uses unsupervised temporal clustering algorithm along with survey analysis to determine infr…
New method assesses energy storage value beyond cost reduction.
problem Improving energy storage value beyond cost reduction.
method Market potential method to evaluate and compare energy storage technologies.
result High-cost hydrogen storage can be more valuable than low-cost hydrogen storage.
In this paper we show how to place Michael Berry's discovery of knotted zeros in the quantum states of hydrogen in the context of general knot theory and in the context of our formulations for quantum knots. Berry gave a time independent wave function for hydrogen, as a map from three space to the complex plane and suc…
Site-specific graph neural network for predicting protonation energy of oxygenate moleculesphysics.chem-ph
Bio-oil molecule assessment is essential for the sustainable development of chemicals and transportation fuels. These oxygenated molecules have adequate carbon, hydrogen, and oxygen atoms that can be used for developing new value-added molecules (chemicals or transportation fuels). One motivation for our study stems fr…
RNA structures show that a significant portion of bases do not form hydrogen bonds.
problem Understanding the unpaired bases in RNA secondary structures.
method Comparing random words in free groups to RNA sequences, analyzing word lengths.
result The expected fraction of unpaired bases converges to a constant .
We extend the Feynman-Kac formula for Schrödinger type operators on vector bundles over noncompact Riemannian manifolds to possibly very singular potentials that appear in hydrogen like quantum mechanical problems and that need not be bounded from below or locally square integrable. This path integral formula is then u…
Path integral approach to the pricing of timer options with the Duru-Kleinert time transformationq-fin.PR
In this paper, a time substitution as used by Duru and Kleinert in their treatment of the hydrogen atom with path integrals is performed to price timer options under stochastic volatility models. We present general pricing formulas for both the perpetual timer call options and the finite time-horizon timer call options…
Alternative proofs for various inequalities on Riemannian manifolds.
problem Various functional inequalities on Riemannian manifolds.
method Generic functional inequality, Riccati pairs, solving Riccati-type ODE.
result Alternative proofs for multiple inequalities, including Hardy-type and Caccioppoli inequalities.
New method preserves unitarity for Schrödinger equation learning, reducing errors and improving time generalization.
problem Learning the evolution operator for time-dependent Schrödinger equation with varying Hamiltonians.
method Linear estimator preserving weak unitarity, with theoretical error bounds and time generalization.
result Achieves up to two orders of magnitude smaller relative errors than existing methods.
CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networkscs.LG
Our main motivation is to propose an efficient approach to generate novel multi-element stable chemical compounds that can be used in real world applications. This task can be formulated as a combinatorial problem, and it takes many hours of human experts to construct, and to evaluate new data. Unsupervised learning me…
Noisy, sparse, nonlinear: Navigating the Bermuda Triangle of physical inference with deep filteringphysics.chem-ph
Capturing the microscopic interactions that determine molecular reactivity poses a challenge across the physical sciences. Even a basic understanding of the underlying reaction mechanisms can substantially accelerate materials and compound design, including the development of new catalysts or drugs. Given the difficult…
A Critical Review of Physics-Informed Machine Learning Applications in Subsurface Energy Systemscs.LG
PIML enhances machine learning for subsurface energy systems.
problem Lack of interpretability and domain-specific knowledge in machine learning models.
method Integrates physics principles into data-driven models using deep learning.
result PIML improves model generalization and adherence to physical laws.
Variational principle to regularize machine-learned density functionals: the non-interacting kinetic-energy functionalphysics.chem-ph
New method trains deep neural networks for non-interacting kinetic-energy functionals in DFT.
problem Lack of exact relationship between electron density and non-interacting kinetic energy.
method Variational principle to regularize machine-learned density functionals.
result Excellent results on kinetic-energy functionals for various systems.
Study analyzes low-energy behavior of Schrödinger operators with Coulomb potentials.
problem Analyzing the limiting resolvent of Schrödinger operators at low energies.
method Using Vasy's second microlocal approach (Lagrangian approach), uniformly analyzing the resolvent from .
result Obtained oscillatory asymptotics for the resolvent output at low energy, differing from short-range cases.
Flow-VQE uses generative flows to optimize VQE efficiently.
problem Complex objective functions and expensive optimization in VQE.
method Generative normalizing flows with parameterized quantum circuits.
result Flow-VQE accelerates convergence and reduces circuit evaluations.
Exploration of hydrocarbon resources is a highly complicated and expensive process where various geological, geochemical and geophysical factors are developed then combined together. It is highly significant how to design the seismic data acquisition survey and locate the exploratory wells since incorrect or imprecise …
21cmEMU speeds up EoR simulations by 10^4x, predicting key observables with sub-percent accuracy.
problem Efficiently simulating and inferring Epoch of Reionization properties from limited computational resources.
method Developed an emulator for 21cmFAST simulation outputs, reducing computational cost by 10^4x.
result Sub-percent accurate predictions of 6 key observables from EoR simulations.
Graph convolutional network (GCN) is generalization of convolutional neural network (CNN) to work with arbitrarily structured graphs. A binary adjacency matrix is commonly used in training a GCN. Recently, the attention mechanism allows the network to learn a dynamic and adaptive aggregation of the neighborhood. We pro…
This study optimizes energy storage scheduling under price uncertainty, balancing risk and reward.
problem Optimizing energy storage operation under price uncertainty and risk.
method Two-stage stochastic risk-constrained approach using conditional value-at-risk.
result Increasing risk aversion leads to substantial benefits in terms of risk reduction and expected reward.