Related papers: Transfer-learning-based Surrogate Model for Therma…
Superlattices are promising low-dimensional nanomaterials for thermoelectric technology that is capable of directly converting low-grade heat energy to useful electrical power. In this work, the thermal conductivities of GaAs/Ge…
We report on thermal transport properties in 2 and 3 dimensions interconnected nanowire networks (strings and nodes). The thermal conductivity of these nanostructures decreases in increasing the distance of the nodes, reaching ultra-low…
Understanding heat transfers in fibrous materials, particularly conduction, is a major challenge due to their heterogeneous and multiscale nature, and the unknown contribution of fiber-to-fiber contacts. In most previous modeling studies,…
Determining onflow parameters is crucial from the perspectives of wind tunnel testing and regular flight and wind turbine operations. These parameters have traditionally been predicted via direct measurements which might lead to challenges…
Two-dimensional (2D) nanoparticle films are a new class of materials with interesting physical properties and applications ranging from nanoelectronics to sensing and photonics. The importance of conducting nanoparticle films makes the…
In a well-dispersed nanofluid with strong cluster-fluid attraction, thermal conduction paths can arise through percolating amorphous-like interfacial structures. This results in a thermal conductivity enhancement beyond the Maxwell limit of…
The prediction of mechanical and thermal properties of polymers is a critical aspect for polymer development. Herein, we discuss the use of transfer learning approach to predict multiple properties of linear polymers. The neural network…
Data assimilation presents computational challenges because many high-fidelity models must be simulated. Various deep-learning-based surrogate modeling techniques have been developed to reduce the simulation costs associated with these…
Parameter estimation for dynamical systems remains challenging due to non-convexity and sensitivity to initial parameter guesses. Recent deep learning approaches enable accurate and fast parameter estimation but do not exploit transferable…
There has been much interest in semiconductor superlattices because of showing very low thermal conductivities. This makes them especially suitable for applications in a variety of devices for thermoelectric generation of energy, heat…
Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target domain data…
The thermal conductivity and stability of nanofluids have posed the biggest challenges to their adoption as coolants in thermal applications in industries such as electronic equipment, heat exchangers, and solar technologies. In this paper,…
With the continuous advancement of nanotechnology, nanodevices have become crucial components in computing, sensing and energy conversion applications. However, the structures of nanodevices typically possess sub-wavelength dimensions and…
Lattice thermal conductivity (TC) of semiconductors is crucial for various applications, ranging from microelectronics to thermoelectrics. Data-driven approach can potentially establish the critical composition-property relationship needed…
Recent advances in the synthesis of inorganic and organic nanowires and nanotubes have provided both components for various functional devices and platforms for the study of low- dimensional transport phenomena. However, tremendous…
Superconductivity is a remarkable phenomenon in condensed matter physics, which comprises a fascinating array of properties expected to revolutionize energy-related technologies and pertinent fundamental research. However, the field faces…
Thomson scattering (TS) diagnostics provide reliable, minimally perturbative measurements of fundamental plasma parameters, such as electron density ($n_e$) and electron temperature ($T_e$). Deep neural networks can provide accurate…
With the popularity of electric vehicles, the demand for lithium-ion batteries is increasing. Temperature significantly influences the performance and safety of batteries. Battery thermal management systems can effectively control the…
Semiconductor nanostructures hold great promise for high-efficiency waste heat recovery exploiting thermoelectric energy conversion, a technological breakthrough that could significantly contribute to providing environmentally friendly…
The performance of machine learning surrogates is critically dependent on data quality and quantity. This presents a major challenge, as high-fidelity (HF) data is often scarce and computationally expensive to acquire, while low-fidelity…