Tag: simulations

This blog was co-authored by Takehiro Nakajima and Mark Azadpour from AWS and Rintaro Yamada, Rei Kajitani and Ryo Kunimoto from Daiichi Sankyo In recent years, the informatics field of drug discovery has seen a rapid increase in workloads requiring large-scale parallel computing, such as genome analysis, structure prediction, and drug design. Daiichi Sankyo has […]

Today we’re excited to announce expanded support for custom Slurm settings in AWS Parallel Computing Service (PCS). With this launch, PCS now enables you to configure over 65 Slurm parameters. And for the first time, you can also apply custom settings to queue resources, giving you partition-specific control over scheduling behavior. This release responds directly […]

Cyclone chasing just got a whole lot smarter! Check out how DTN’s AI-powered weather model is rewriting the forecast. Brace yourself for the future of weather prediction.

When seeking to improve machine learning model performance, hyperparameter tuning is often the go-to recommendation. However, this approach faces significant limitations, particularly for complex models requiring extensive training times. In this post, we’ll explore a novel approach that combines gradient norm analysis with Large Language Model (LLM) guidance to intelligently redesign neural network architectures. This […]

Boost ASIC verification efficiency with SilverLining EDA’s VeriFire on Amazon EC2 F2 Instances: Unlock up to 60% better price-performance compared to F1, and accelerate your FPGA build times by 80% with SilverLining EDA’s cloud-based emulation solution.

Subsurface infrastructure mapping is crucial for industries ranging from oil and gas to environmental protection. Our groundbreaking approach combines advanced magnetic imaging with physics-informed AI to provide unparalleled visibility into hidden structures, even when traditional methods fall short. Explore how this fusion of cloud computing and AI is opening new possibilities for subsurface exploration and management.

In this post, we’ll examine how this multi-agent approach works, the architecture behind it, and the efficiency improvements it enables. While the focus is on an engineering use case, the principles apply broadly to any organization facing the challenge of coordinating specialized expertise to deliver faster, more consistent results.

Ansys Gateway powered by AWS now has an integration with AWS ParallelCluster to enable users deploy on-demand HPC clusters for running Ansys simulations on AWS. This allows engineers to run large-scale simulations efficiently while optimizing cloud costs by dynamically adjusting resources based on simulation workload requirements. In this blog post, we describe the architecture, workflow, and Amazon EC2 recommendations for running Ansys applications in Ansys Gateway.

Autonomous robots are increasingly used across industries, from warehouses to space exploration. While developing these robots requires complex simulation and reinforcement learning (RL), setting up training environments can be challenging and time-consuming. AWS Batch multi-node parallel (MNP) infrastructure, combined with NVIDIA Isaac Lab, offers a solution by providing scalable, cost-effective robot training capabilities for sophisticated behaviors and complex tasks.

Developing robust investment strategies requires thorough testing, but relying solely on historical data can introduce biases and limit your insights. Learn how synthetic data from agent-based models can provide an unbiased testbed to systematically evaluate your strategies and prepare for future market scenarios. Part 2 covers implementation details and results.