Simulation-Driven Graphics and Fabrication

计算物理的算法充当了计算机的想象力，允许机器将复杂的、现实世界中相互作用的结果“可视化”。这些算法被用来驱动我们玩的游戏，我们看的电影，设计汽车、轮船和飞机，以及从建筑到医学的无数其他领域。但是，尽管一个人可以想象一个物体几乎瞬间与世界互动，但精确的物理模拟是在几小时、几天或几周的时间尺度上进行的。计算制造和计算机图形学放大了对像人一样快的物理模拟的需求。加法制造(被世界经济论坛确定为一项关键的新兴技术)允许对装配物的几何和材料属性进行超高分辨率控制。预测这种设计在现实世界中的性能通常需要解决比以前在工程学科中所要求的大几千倍的计算问题。莱文教授研究的总体目标是通过开发理解物理和力学的新机器学习技术来体现这种计算想象力。他的工作解决了将标准机器学习技术应用于物理模拟中的大规模问题的主要困难。Levin专注于创建对真实世界输入数据稳健的算法，从自动驾驶汽车产生的LIDAR数据，到工程设计过程中创建的计算机辅助设计。通过利用这些不断增加的几何信息源，Levin利用学习和数据驱动的方法来创建用于物理模拟的快速算法，该算法可以比标准方法快几个数量级，但仍保持预测精度。这些方法使可伸缩的生成性设计能够跨越无数的工程和计算机图形领域--从航空设计到视觉效果。最后，莱文的工作重点是可在几何和现象学的大家族中推广的方法，这些方法可以减少为新示例训练神经模型的需要--节省计算能力和能量。莱文教授的研究实现了模拟性能和准确性的改变，以及比其他学科更环保的机器学习方法。

Algorithms for computational physics serve as a computer's imagination, allowing the machine to ``visualize'' the outcome of complex, real-world interactions. These algorithms are used to drive the games we play, the movies we watch, to design cars, ships and planes and in countless other areas from architecture to medicine. But, while a person can imagine an object interacting with the world almost instantaneously, accurate physical simulations operate on time scales of hours, days or weeks. Computational Fabrication and Computer Graphics amplify the need for as-fast-as-a-person physics simulation. Additive manufacturing (identified by the World Economic Forum as a key emerging technology) allows for the ultra high-resolution control of geometry and material properties of fabricated objects. Predicting the performance of such designs in the real world often requires the solution of computational problems thousands of times larger than what was previously required in engineering disciplines. The over-arching goal of Professor Levin's research is to manifest this computational imagination by developing new machine learning techniques that understand physics and mechanics. His work tackles major difficulties in adapting standard machine learning techniques to large-scale problems in physics simulation. Levin focuses on creating algorithms that are robust to real-world input data, from LIDAR data generated by self-driving cars, to computer-aided designs created during the engineering design process. By leveraging these increasing sources of geometric information, Levin utilizes learning and data-driven approaches to create fast algorithms for physics simulation that can be orders-of-magnitude faster than standard approaches, but retain predictive accuracy. These methods enable scalable generative design across a myriad of engineering and computer graphics domains -- from aeronautical design to visual effects. Finally, Levin's work focuses on methods that are generalizable across large families of geometries and phenomenologies which can alleviate the need to train neural models for new examples -- conserving computational power and energy. Professor Levin's research enables a game changing shift in simulation performance and accuracy along with more environmentally friendly machine learning approaches than in other disciplines.