FLUID INTELLIGENCE CONSISTS OF UPDATE
FLUID INTELLIGENCE CONSISTS OF SOFTWARE

The latest version of Modulus has added support for RNN type layers and models. In physics-ML, these models are critical in predicting dynamic physical systems’ evolution, enabling better simulations, understanding of complex natural phenomena, and aiding in discoveries. These models are unique in their ability to capture temporal dependencies and learn complex patterns over time, making them well suited for forecasting time varying relationships. The application of deep learning architectures-particularly RNNs, long short-term memory networks (LSTMs), and similar networks-has significantly enhanced the predictive capabilities. Time-series prediction is a key task in many domains. It also provides several optimizations including CuGraphOps support, fused layer norm and Adam optimizer using Apex, efficient edge feature updates, and more. The GraphCast implementation supports gradient checkpointing for reducing the memory overhead. The out-of-sample prediction for the 2-meter temperature using a GraphCast model trained in Modulus on a 34-variable subset of the ERA-5 dataset Figure 1 shows the out-of-sample prediction for the 2-meter temperature using a GraphCast model trained in Modulus on a 34-variable subset of the ERA-5 dataset. A recipe for training GraphCast on the ERA-5 dataset with support for data parallelism is provided in Modulus-Launch. The multi-mesh is a set of icosahedral meshes with increasing resolution, providing uniform resolution across the globe. The processor updates each multi-mesh node using learned message-passing.įinally, the decoder maps the processed multi-mesh features back onto the grid representation. The encoder maps the local regions of the grid’s latent features into nodes of a multi-mesh graph representation. The embedder component embeds the input features into a latent representation.

The architecture of GraphCast consists of four main components: embedder, encoder, processor, and decoder.

This graph-based representation enables the model to capture both local and non-local dependencies in the data. The weather data is modeled as a graph, where nodes represent Earth grid cells. It significantly improves on some existing models by effectively capturing spatio-temporal relationships in weather data. GraphCast is a novel GNN-based architecture for global weather forecasting. Modulus also includes the GraphCast architecture proposed in GraphCast: Learning Skillful Medium-Range Global Weather Forecasting. This pretrained model is available through NVIDIA NGC. Such architectures can now be trained in Modulus, which includes a MeshGraphNet model pretrained on a parameterized vortex shedding dataset. Modulus includes recipes that use the MeshGraphNet architecture based on the work presented in Learning Mesh-Based Simulation with Graph Networks. This enables you to develop your own GNN-based models for specific use cases. The latest version of NVIDIA Modulus includes support for GNNs. Using GNNs, researchers can better understand the behavior of complex systems with complex geometries, and generate more accurate predictions based on the learned patterns and interactions within the data. This capability is useful in applications such as computational fluid dynamics, molecular dynamics simulations, and material science. Through the application of GNNs, researchers can model systems to be represented as graphs or meshes.

By leveraging the structure of graphs, GNNs are capable of learning and making predictions based on the relationships among nodes in a graph. GNNs are transforming how researchers are addressing challenges involving intricate graph structures, such as those encountered in physics, biology, and social networks.

FLUID INTELLIGENCE CONSISTS OF UPDATE

Two major components of this update are 1) supporting new network architectures that include graph neural networks (GNNs) and recurrent neural networks (RNNs), and 2) improving the ease of use for AI practitioners.

FLUID INTELLIGENCE CONSISTS OF SOFTWARE

The latest Modulus software update, version 23.05, brings together new capabilities, empowering the research community and industries to develop research into enterprise-grade solutions through open-source collaboration. Modulus is available as OSS (Apache 2.0 license) to support the growing physics-ML community. NVIDIA Modulus is a framework for building, training, and fine-tuning deep learning models for physical systems, otherwise known as physics-informed machine learning (physics-ML) models.

Fluid intelligence consists of