In the context of science, the well-known adage "a picture is worth a thousand words" might well be "a model is worth a thousand datasets." Scientific models, such as Newtonian physics or biological gene regulatory networks, are human-driven simplifications of complex phenomena that serve as surrogates for the countless experiments that validated the models. Recently, machine learning has been able to overcome the inaccuracies of approximate modeling by directly learning the entire set of nonlinear interactions from data. However, without any predetermined structure from the scientific basis behind the problem, machine learning approaches are flexible but data-expensive, requiring large databases of homogeneous labeled training data. A central challenge is reconciling data that is at odds with simplified models without requiring "big data". In this work we develop a new methodology, universal differential equations (UDEs), which augments scientific models with machinelearnable structures for scientifically-based learning. We show howUDEs can be utilized to discover previously unknown governing equations, accurately extrapolate beyond the original data, and accelerate model simulation, all in a time and data-efficient manner. This advance is coupled with open-source software that allows for training UDEs which incorporate physical constraints, delayed interactions, implicitly-defined events, and intrinsic stochasticity in the model. Our examples show how a diverse set of computationallydifficult modeling issues across scientific disciplines, from automatically discovering biological mechanisms to accelerating climate simulations by 15,000x, can be handled by training UDEs.

Even with today's immense computational resources, climate models cannot resolve every cloud in the atmosphere or eddying swirl in the ocean. However, collectively these small-scale turbulent processes play a key role in setting Earth's climate. Climate models attempt to represent unresolved scales via surrogate models known as parameterizations. These have limited fidelity and can exhibit structural deficiencies. Here we demonstrate that neural differential equations (NDEs) may be trained by highly resolved fluid-dynamical models of the scales to be parameterized and those NDEs embedded in an ocean model. They can incorporate conservation laws and are stable in time. We argue that NDEs provide a new route forward to the development of surrogate models for climate science, opening up exciting new opportunities.Preprint. Under review.

Software is known as one of the components found on a computer. As a software, computer-aided design can be used as a tool to realize ideas. In the process, to be able to realize the idea, it is necessary to have a visual simulation as an aid to be able to see conditions that are able to represent the results of the ideas made. By using qualitative descriptive methods. this research will be useful to be able to provide information to users of computer-aided design software as a basis for using software to produce visual simulations Computer-aided design software in its development can help users to be able to see the condition of a design in a form that represents the original shape. and also can minimize the use of real prototypes to deliver design results. Keywords: Devices, Software, computer-aided design, Simulation, Visual.

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Kemasan merupakan salah satu cara untuk memberikan informasi mengenai produk yang terdapat di dalamnya, sedangkan informasi diharapkan dapat memberikan keterangan dengan jelas. Dalam kemasan mainan, terkadang informasi yang diberikan didominasi oleh gambar produknya. Penelitian menggunakan metode kualitatif yang bertujuan untuk memberikan informasi berdasarkan kepada penggambaran yang terdapat di kemasan khususnya kemasan mainan flying glider. Dengan pendekatan berupa studi kasusu diharapkan akan memberikan jawaban yang dapat membantu dalam melihat visual kemasan mainan dalam memberikan informasi yang terkait dengan produk yang dikemasnya. Visual dari kemasan mainan flying glider lebih didominasi oleh penggunaan gambar sebagai informasi yang menjelaskan mengenai isi dari kemasan tersebut. Sedangkan teks sebagai salah satu cara menyampaikan informasi digunakan sebagai pelengkap penjelasan dari mainan. Kata kunci: Visual, kemasan, flying glider, informasi.