309 research outputs found
Multi-Agent Combinatorial Path Finding with Heterogeneous Task Duration
Multi-Agent Combinatorial Path Finding (MCPF) seeks collision-free paths for
multiple agents from their initial locations to destinations, visiting a set of
intermediate target locations in the middle of the paths, while minimizing the
sum of arrival times. While a few approaches have been developed to handle
MCPF, most of them simply direct the agent to visit the targets without
considering the task duration, i.e., the amount of time needed for an agent to
execute the task (such as picking an item) at a target location. MCPF is
NP-hard to solve to optimality, and the inclusion of task duration further
complicates the problem. This paper investigates heterogeneous task duration,
where the duration can be different with respect to both the agents and
targets. We develop two methods, where the first method post-processes the
paths planned by any MCPF planner to include the task duration and has no
solution optimality guarantee; and the second method considers task duration
during planning and is able to ensure solution optimality. The numerical and
simulation results show that our methods can handle up to 20 agents and 50
targets in the presence of task duration, and can execute the paths subject to
robot motion disturbance
Data-driven learning of the generalized Langevin equation with state-dependent memory
We present a data-driven method to learn stochastic reduced models of complex
systems that retain a state-dependent memory beyond the standard generalized
Langevin equation (GLE) with a homogeneous kernel. The constructed model
naturally encodes the heterogeneous energy dissipation by jointly learning a
set of state features and the non-Markovian coupling among the features.
Numerical results demonstrate the limitation of the standard GLE and the
essential role of the broadly overlooked state-dependency nature in predicting
molecule kinetics related to conformation relaxation and transition
Size and temperature effects on the viscosity of water inside carbon nanotubes
The influences of the diameter (size) of single-walled carbon nanotubes (SWCNTs) and the temperature on the viscosity of water confined in SWCNTs are investigated by an "Eyring-MD" (molecular dynamics) method. The results suggest that the relative viscosity of the confined water increases with increasing diameter and temperature, whereas the size-dependent trend of the relative viscosity is almost independent of the temperature. Based on the computational results, a fitting formula is proposed to calculate the size- and temperature- dependent water viscosity, which is useful for the computation on the nanoflow. To demonstrate the rationality of the calculated relative viscosity, the relative amount of the hydrogen bonds of water confined in SWCNTs is also computed. The results of the relative amount of the hydrogen bonds exhibit similar profiles with the curves of the relative viscosity. The present results should be instructive for understanding the coupling effect of the size and the temperature at the nanoscale
Error estimates of residual minimization using neural networks for linear PDEs
We propose an abstract framework for analyzing the convergence of
least-squares methods based on residual minimization when feasible solutions
are neural networks. With the norm relations and compactness arguments, we
derive error estimates for both continuous and discrete formulations of
residual minimization in strong and weak forms. The formulations cover recently
developed physics-informed neural networks based on strong and variational
formulations
Cross-correlated quantum thermometry using diamond containing dual-defect centers
The contactless temperature measurement at micro/nanoscale is vital to a
broad range of fields in modern science and technology. The nitrogen vacancy
(NV) center, a kind of diamond defect with unique spin-dependent
photoluminescence, has been recognized as one of the most promising
nanothermometers. However, this quantum thermometry technique has been prone to
a number of possible perturbations, which will unavoidably degrade its actual
temperature sensitivity. Here, for the first time, we have developed a
cross-validated optical thermometry method using a bulk diamond sample
containing both NV centers and silicon vacancy (SiV) centers. Particularly, the
latter allowing all-optical method has been intrinsically immune to those
influencing perturbations for the NV-based quantum thermometry, hence serving
as a real-time cross validation system. As a proof-of-concept demonstration, we
have shown a trustworthy temperature measurement under the influence of varying
magnetic fields. This multi-modality approach allows a synchronized
cross-validation of the measured temperature, which is required for
micro/nanoscale quantum thermometry in complicated environments such as a
living cell
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