Our Insights

At Inductiva, we are committed to supporting academic teams that are tackling real-world engineering challenges through simulation. MotoStudent FEUP, a student-led team, is building an electric racing motorcycle for the MotoStudent Electric competition, a project requiring advanced CFD (Computational Fluid Dynamics) to analyse how aerodynamics would affect the motorcycle’s speed, stability, and structure.

The Inductiva Team

MotoStudent FEUP

Lots of improvements on this v0.17 release: some are “invisible”, but mission critical, such as the platform improvements on security and scalability, others you will notice right away, such as the new awesome Benchmarks Dashboard or the Tasks’ System Metrics. Below, we’ll dive deeper into how to use these features to help you run simulations more efficiently and cost-effectively, and also breakdown when and why to use each of them.

The Inductiva Team

In this edition of Collaborative Insights, we’re proud to share a project developed with Rui Coelho, Principal Investigator at IPMA (Portuguese Institute for the Ocean and Atmosphere). Rui and his team used Inductiva’s cloud-based HPC platform to dramatically accelerate their work in stock assessment modeling for the South Atlantic shortfin mako shark—a species whose conservation depends on rigorous science and timely insights.

The Inductiva Team

Rui Coelho (IPMA, Principal Investigator)

At Inductiva.AI, our mission is to empower engineers and scientists to solve complex physical problems at scale. Recently, we partnered with the Porto Space Team, a student rocketry group designing a novel airbrake system for their EuRoC competition rocket, a project requiring advanced CFD (Computational Fluid Dynamics) analyses over a wide transonic regime.

The Inductiva Team

Porto Space Team

Inductiva joined the 1st OpenFOAM HPC Challenge to test how cloud infrastructure stacks up against traditional HPC for large-scale CFD simulations. Running the DrivAer automotive benchmark, the team explored multiple hardware setups, hyperthreading choices, and domain decomposition strategies. The results? Inductiva’s flexible MPI clusters handled up to 768 partitions with impressive price-performance—even outperforming pricier hardware in some cases. For simulations below massive supercomputer scales, cloud HPC proves not only competitive but cost-effective, offering engineers and researchers agility without sacrificing speed. Curious how to fine-tune your OpenFOAM workloads in the cloud? Dive into the benchmarks and see what’s possible.

Luís Sarmento

Everyone’s talking about LLMs, but the real next wave is AI for the physical world—AI that understands fluid dynamics, thermodynamics, electromagnetism, and even quantum physics. From breakthroughs like DeepMind’s AlphaFold in protein folding to NVIDIA’s AI weather forecasting, industries from renewable energy to materials science are being transformed. But experimental data isn’t always enough; numerical simulations are crucial to generate high-quality datasets that power scientific AI. At Inductiva.AI, we’re offering a computational platform to democratize physics dataset generation, combining open-source simulators with cloud HPC, making it as easy as running a Python script. Explore how AI and scientific computing are converging—and what it means for the future of innovation.

Hugo Penedones