Jorge Amador, a PhD student at KAUST's Visual Computing Center, presented a talk on physically-based simulation for generative AI models. The talk covered the use of synthetic data generation and physical priors to address the need for high-quality datasets. Applications discussed include photo editing, navigation, digital humans, and cosmological simulations. Why it matters: This research explores a promising technique to overcome data scarcity issues in AI, particularly relevant in resource-constrained environments or for sensitive applications.
This paper introduces a virtual wheel-terrain interaction model developed and validated for the UAE Rashid rover to enhance simulation accuracy for space rovers. The model incorporates wheel grouser properties, slippage, soil properties, and interaction mechanics, validated via lunar soil simulation. Experiments tested a Grouser-Rashid rover wheel at slip ratios of 0, 0.25, 0.50, and 0.75. Why it matters: This simulation method advances rover design and control, crucial for the UAE's space exploration program and lunar mission success.
Fudan University's Zhongyu Wei presented research on social simulation driven by LLMs, covering individual and large-scale social movement simulation. Wei directs the Data Intelligence and Social Computing Lab (Fudan DISC) and has published extensively on multimodal large models and social computing. His work includes the Volcano multimodal model, DISC-MedLLM, and ElectionSim. Why it matters: Using LLMs for social simulation could provide new tools for understanding and potentially predicting social dynamics in the Arab world.
Nobuyuki Umetani from the University of Tokyo presented a talk on using AI to accelerate simulations and optimization for 3D shape designs. The talk covered interactive approaches integrating physical simulation into geometric modeling. Specific applications discussed included musical instruments, garment design, aerodynamic design, and floor plan design. Why it matters: This highlights growing interest in AI techniques at MBZUAI and across the GCC for streamlining engineering design and simulation processes.
QRC has developed Qibo, a Python library enabling classical simulation of quantum algorithms with double precision. Qibo leverages hardware accelerators like GPUs and CPUs with multi-threading. It incorporates a multi-GPU distributed approach for circuit simulation. Why it matters: This framework allows researchers and developers in the region to explore and prototype quantum algorithms using existing classical computing infrastructure, fostering innovation in quantum computing research and applications.
Patrick van der Smagt, Director of AI Research at Volkswagen Group, discussed the use of generative machine learning models for predicting and controlling complex stochastic systems in robotics. The talk highlighted examples in robotics and beyond and addressed the challenges of achieving quality and trust in AI systems. He also mentioned his involvement in a European industry initiative on trust in AI and his membership in the AI Council of the State of Bavaria. Why it matters: Understanding control in robotics, along with trust in AI, are key issues for further development of autonomous systems, especially in industrial applications within the GCC region.
KAUST's Core Labs provide engineering simulation services and training using state-of-the-art technology. The Supercomputing Core Lab (KSL) at KAUST conducts training workshops in partnership with ANSYS, a market leader in engineering and simulation design software. Since 2017, KSL has conducted five training workshops related to engineering software in partnership with ANSYS, with 230 attendees, including 138 individuals from in-Kingdom institutions outside of KAUST. Why it matters: These workshops strengthen Saudi Arabia's engineering capabilities by providing access to simulation software and training, facilitating collaboration between KAUST, Saudi Aramco, and SABIC.
Tailin Wu from Stanford presented research on using machine learning to accelerate scientific discovery and simulation at MBZUAI. The work covers learning theories from dynamical systems with improved accuracy and interpretability. It also introduces LAMP, a deep learning model optimizing spatial resolutions in simulations. Why it matters: Efficient AI-driven scientific simulation has broad implications for research in physics, biomedicine, materials science and engineering across the region.