概括
一个新的深度学习模型准确地预测了Tm/Ho:YAP晶体激光在热负荷下的性能. 这种先进的框架提供了精确的输出功率预测,改善了固态激光模型和优化.
科学领域:
- 固态激光物理 固态激光物理
- 计算建模计算建模
- 材料科学是一种材料科学.
背景情况:
- 传统模型在强烈的热光学合下与Tm/Ho:YAP激光器作斗争.
- 精确预测激光输出性能对于优化至关重要.
研究的目的:
- 开发一种用于精确建模Tm/Ho:YAP集成晶体在复杂热负荷下的新型框架.
- 为了提高在存在显著的热光学效应的情况下,激光输出功率的预测.
主要方法:
- 一个双分支深度回归框架,结合了残余卷积神经网络 (ResNet) 和多层感知子 (MLP).
- 结晶温度分布图像和关键实验参数的联合分析.
- 在复杂的热负荷下高精度捕获激光动态.
主要成果:
- 拟议的模型在输出功率预测方面显著优于现有的方法.
- 在双波长操作时,预测误差低至0.58%.
- 在复杂的热负荷下捕获激光动态的高精度.
结论:
- 双分支深度回归框架为模拟和优化热光合固态激光器提供了新的视角.
- 该模型增强了在具有挑战性的热条件下对激光性能的理解和控制.
- 在固态激光系统中提供了一条提高效率和可靠性的途径.
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