Language model intelligence is revolutionizing the way we program materials
simulations. However, the diversity of simulation scenarios renders it
challenging to precisely transform human language into a tailored simulator.
Here, using three functionalized types of language model, we propose a
language-to-simulation (Lang2Sim) framework that enables interactive navigation
on languaging a simulation engine, by taking a scenario instance of water
sorption in porous matrices. Unlike line-by-line coding of a target simulator,
the language models interpret each simulator as an assembly of invariant tool
function and its variant input-output pair. Lang2Sim enables the precise
transform of textual description by functionalizing and sequentializing the
language models of, respectively, rationalizing the tool categorization,
customizing its input-output combinations, and distilling the simulator input
into executable format. Importantly, depending on its functionalized type, each
language model features a distinct processing of chat history to best balance
its memory limit and information completeness, thus leveraging the model
intelligence to unstructured nature of human request. Overall, this work
establishes language model as an intelligent platform to unlock the era of
languaging a simulation engine.

利用三个不同类型的语言模型，该研究提出了一种语言到模拟引擎的框架，能精确地转换人类语言到定制的模拟器，从而实现水吸附在多孔基质中的场景实例的交互式导航。

语言化仿真引擎

On Languaging a Simulation Engine

Deep learning has rapidly transformed the state of the art algorithms used to
address a variety of problems in computer vision and robotics. These
breakthroughs have relied upon massive amounts of human annotated training
data. This time consuming process has begun impeding the progress of these deep
learning efforts. This paper describes a method to incorporate photo-realistic
computer images from a simulation engine to rapidly generate annotated data
that can be used for the training of machine learning algorithms. We
demonstrate that a state of the art architecture, which is trained only using
these synthetic annotations, performs better than the identical architecture
trained on human annotated real-world data, when tested on the KITTI data set
for vehicle detection. By training machine learning algorithms on a rich
virtual world, real objects in real scenes can be learned and classified using
synthetic data. This approach offers the possibility of accelerating deep
learning's application to sensor-based classification problems like those that
appear in self-driving cars. The source code and data to train and validate the
networks described in this paper are made available for researchers.

这篇研究论文提出了一种使用仿真引擎产生的合成注释数据的方法，以训练机器学习算法。通过在丰富的虚拟环境中训练机器学习算法来实现对真实世界中的物体的分类，为实现自动驾驶汽车等基于传感器分类问题的深度学习应用提供了可能性。